Facial Recognition Using Radio Frequency Detection

Abstract

Systems and techniques are disclosed for detecting user presence, user movement, and performing facial recognition. For example, a wireless device can receive a waveform that is a reflection of a transmitted radio frequency (RF) waveform. The wireless device can determine the presence of the user based on RF sensing data associated with the received waveform. In response to determining the presence of the user, the wireless device can initiate facial recognition of the user.

Description

[Technical Field]

[0001] FIELD OF THE DISCLOSURE The present disclosure relates generally to detecting the presence of a user and / or performing facial recognition. Aspects of the present disclosure relate to systems and techniques for detecting the presence of a user and / or performing facial recognition using radio frequency (RF) detection. [Background technology]

[0002] Wireless electronic devices may provide security features that can be used to prevent unauthorized access to the device. For example, a portable electronic device may include software and hardware components that can place the wireless device in a "locked" state that prevents unauthorized users from accessing the device.

[0003] The wireless electronic device may further include hardware and software components that can be used to unlock the device based on biometric features associated with an authorized user, such as facial recognition or fingerprint recognition. To perform various telecommunications functions, the wireless electronic device may include hardware and software components configured to transmit and receive radio frequency (RF) signals. For example, the wireless device may be configured to communicate via Wi-Fi, 5G / New Radio (NR), Bluetooth™, and / or Ultra Wideband (UWB), among others. Summary of the Invention [Means for solving the problem]

[0004] The following presents a simplified summary of one or more aspects disclosed herein. As such, the following summary is not intended to be an extensive overview of all contemplated aspects, nor is it intended to identify key or critical elements of all contemplated aspects or to delineate the scope associated with any particular aspect. Thus, the sole purpose of the following summary is to present some concepts of one or more aspects of the mechanisms disclosed herein in a simplified form prior to the detailed description presented below.

[0005]

[0006] A system, method, apparatus, and computer-readable medium for performing facial recognition are disclosed. According to at least one example, a method for performing facial recognition is provided. The method can include receiving, by a first wireless device, a first received waveform that is a reflection of a first radio frequency (RF) waveform, determining a presence of a user based on RF sensing data associated with the first received waveform, and initiating facial authentication of the user in response to determining the presence of the user.

[0006] In another example, a wireless device for facial recognition is provided, including at least one transceiver, at least one memory, and at least one processor coupled to (e.g., configured in circuit with) the at least one memory and the at least one transceiver, wherein the at least one processor is configured to receive, via the at least one transceiver, a first received waveform that is a reflection of a first radio frequency (RF) waveform, determine a presence of a user based on RF sensing data associated with the first received waveform, and initiate facial authentication of the user in response to determining the presence of the user.

[0007] In another example, a non-transitory computer-readable medium having stored thereon at least one instruction that, when executed by one or more processors, causes the one or more processors to receive, by a first wireless device, a first received waveform that is a reflection of a first radio frequency (RF) waveform; determine a presence of a user based on RF sensing data associated with the first received waveform; and initiate facial authentication of the user in response to determining the presence of the user.

[0008] In another example, an apparatus for performing facial recognition is provided, the apparatus including: means for receiving a first received waveform that is a reflection of a first RF waveform; means for determining a presence of a user based on RF sensing data associated with the first received waveform; and means for initiating facial authentication of the user in response to determining the presence of the user.

[0009] In another example, a method for determining a user presence is provided that can include processing, by a wireless device, a first received waveform that is a reflection of a first radio frequency (RF) waveform, determining a user presence based on RF sensing data associated with the first received waveform, transmitting a second RF waveform having a higher bandwidth than the first RF waveform in response to determining the user presence, processing the second received waveform that is a reflection of the second RF waveform from the user, and determining at least one of a presence of the user's head or an orientation of the user's head based on the RF sensing data associated with the second received waveform.

[0010] In another example, a wireless device for determining a user presence is provided, the wireless device including at least one transceiver, at least one memory, and at least one processor coupled to (e.g., configured in circuit with) the at least one memory and the at least one transceiver. The at least one processor is configured to: process, by the wireless device, a first received waveform that is a reflection of a first radio frequency (RF) waveform; determine a user presence based on RF detection data associated with the first received waveform; transmit, via the at least one transceiver, a second RF waveform having a higher bandwidth than the first RF waveform in response to determining the user presence; process the second received waveform that is a reflection of the second RF waveform from the user; and determine at least one of a user head presence or an orientation of the user's head based on the RF detection data associated with the second received waveform.

[0011] In another example, a non-transitory computer-readable medium having at least one instruction stored thereon, the instruction, when executed by one or more processors, causes the one or more processors to: process, by a wireless device, a first received waveform that is a reflection of a first radio frequency (RF) waveform; determine a presence of a user based on RF sensing data associated with the first received waveform; in response to determining the presence of the user, transmit, via at least one transceiver, a second RF waveform having a higher bandwidth than the first RF waveform; process the second received waveform that is a reflection of the second RF waveform from the user; and determine at least one of a presence of a user's head or an orientation of the user's head based on the RF sensing data associated with the second received waveform.

[0012] In another example, an apparatus for determining the presence of a user is provided, the apparatus including: means for processing a first received waveform that is a reflection of a first radio frequency (RF) waveform; means for determining the presence of the user based on RF sensing data associated with the first received waveform; means for transmitting a second RF waveform having a higher bandwidth than the first RF waveform in response to determining the presence of the user; means for processing the second received waveform that is a reflection of the second RF waveform from the user; and means for determining at least one of the presence of the user's head or an orientation of the user's head based on the RF sensing data associated with the second received waveform.

[0013] In some aspects, the apparatus is or is part of a wireless device, such as a mobile device (e.g., a mobile phone or so-called "smartphone" or other mobile device), a wearable device, an extended reality device (e.g., a virtual reality (VR) device, an augmented reality (AR) device, or a mixed reality (MR) device), a tablet, a personal computer, a laptop computer, a server computer, a wireless access point, a vehicle or a component of a vehicle, or any other device with an RF interface.

[0014] Other objects and advantages associated with the embodiments disclosed herein will become apparent to those skilled in the art based on the accompanying drawings and detailed description.

[0015] The accompanying drawings are presented to aid in the explanation of various aspects of the present disclosure and are provided only to illustrate, not limit, the aspects. [Brief explanation of the drawings]

[0016] [Figure 1] FIG. 1 is a block diagram illustrating an example of a computing system for a user device, according to some examples. [Figure 2]FIG. 1 illustrates an example of a wireless device that utilizes radio frequency (RF) sensing techniques to detect the presence of a user and perform facial recognition, according to some examples. [Figure 3] FIG. 1 illustrates an example environment including a wireless device that detects the presence of a user and performs facial recognition, according to some examples. [Figure 4] 1 is a flow diagram illustrating an example process for performing facial recognition, according to some examples. [Figure 5] 1 is a flow diagram illustrating an example process for performing facial recognition, according to some examples. [Figure 6] 1 is a flow diagram illustrating another example of a process for performing facial recognition, according to some examples. [Figure 7] 1 is a flow diagram illustrating another example of a process for performing facial recognition, according to some examples. [Figure 8] 1 is a flow diagram illustrating another example of a process for performing facial recognition, according to some examples. [Figure 9] 1 is a flow diagram illustrating an example process for performing device management based on user attention, according to some examples. [Figure 10] 1 is a flow diagram illustrating another example of a process for performing facial recognition, according to some examples. [Figure 11] 1 is a flow diagram illustrating an example process for performing head detection, according to some examples. [Figure 12] FIG. 1 is a block diagram illustrating an example of a computing system, according to some examples. DETAILED DESCRIPTION OF THE INVENTION

[0017] Some aspects and embodiments of the present disclosure are provided below for illustrative purposes. Alternative aspects may be devised without departing from the scope of the present disclosure. Additionally, well-known elements of the present disclosure will not be described in detail or will be omitted so as not to obscure the relevant details of the present disclosure. As will be apparent to one skilled in the art, some of the aspects and embodiments described herein may be applied independently, and some of them may be applied in combination. In the following description, for purposes of explanation, specific details are set forth to provide a thorough understanding of the embodiments of the present application. However, it will be apparent that various embodiments may be practiced without these specific details. The figures and description are not intended to be limiting.

[0018] The following description provides exemplary embodiments and is not intended to limit the scope, applicability, or configuration of the present disclosure. Rather, the following description of exemplary embodiments provides those skilled in the art with an enabling description for implementing the exemplary embodiments. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the present application, as set forth in the appended claims.

[0019] Many portable electronic devices, such as smartphones, tablets, and laptops, are capable of performing facial recognition. For example, portable electronic devices may utilize facial recognition to perform authentication, such as to verify user identity (e.g., to verify whether the attempted device access is registered in a database of authorized users). Facial recognition has numerous uses, particularly for device access control, e.g., to “unlock” access to a device to enable access to a particular application or service.

[0020] In other examples, facial recognition may be used by a portable electronic device to perform display management functions based on a user's attention to the device. For example, a front-facing sensor (e.g., a dot projector and / or camera) on the device may be used to facilitate facial recognition, for example, to initialize and / or persist the device display for as long as the user is looking at the screen. Other examples of device actions that may be based on user attention include automatically changing display brightness levels, device "lock" timeouts, and / or adjusting alert volume.

[0021] Some existing facial recognition systems utilize an infrared (IR) light source to illuminate a user's face and an infrared (IR) camera to perform image capture. In some cases, the captured image can then be processed and compared to stored, registered faces to perform user authentication. While existing facial recognition systems are generally reliable, such systems can consume a lot of power. To address this issue, facial recognition systems on wireless devices are often triggered only when some type of user activity or predetermined condition is detected, such as a tap on the screen, movement of the device, or an incoming call notification. In the absence of these triggers, the facial recognition system is disabled when the device is locked to conserve battery life. Therefore, existing systems have inherent latency in performing facial recognition to authenticate a user and "unlock" the device.

[0022] The high power consumption of existing facial recognition systems is also an issue when implementing user recognition functions for display management. Such functions may not require the level of accuracy required for facial recognition systems used for face authentication, but in the absence of more efficient alternatives, existing systems must be used, adversely affecting the device's battery life.

[0023] In addition to issues related to high power consumption, some facial recognition systems are unable to function properly when exposed to direct light sources such as sunlight. This is because intense incident light can interfere with IR image fidelity. Another issue is that existing facial recognition systems can malfunction if a user wears certain eyeglasses that filter or block IR light. Furthermore, existing facial recognition systems can be prone to incorrectly authenticating a user based on a photograph of the user.

[0024] It would be desirable to develop techniques that enable devices to perform facial recognition that reduce activation latency while also improving power management to reduce overall power consumption and conserve battery life. Furthermore, it would be desirable to develop techniques that eliminate issues with facial recognition in direct sunlight or with any type of eyeglasses and reduce the likelihood of false authentications. Furthermore, it would be desirable to implement these techniques utilizing existing radio frequency (RF) interfaces on devices.

[0025] Described herein are systems, apparatus, processes (also referred to as methods), and computer-readable media (collectively referred to herein as "systems and techniques") for performing facial recognition. Although the systems and techniques are described herein with respect to facial recognition, the systems and techniques can be used to perform recognition of other body parts and / or objects, such as walls, furniture, doors, etc. The systems and techniques described herein can also be used to track users, animals, objects, etc.

[0026] The systems and techniques provide the ability for electronic devices to collect RF sensing data that can be used to perform facial recognition, detect movement, determine the presence of a user's head and / or other body parts (e.g., parts of the head, face, head and neck region, hands, eyes, etc.), determine the orientation of the user's face, and / or perform facial authentication. In some aspects, the RF sensing data can be collected by utilizing a wireless interface that can simultaneously perform transmitting and receiving functions (e.g., a monostatic configuration). In other aspects, the RF sensing data can be collected by utilizing a bistatic configuration in which the transmitting and receiving functions are performed by different devices (e.g., a first wireless device transmits an RF waveform and a second wireless device receives the RF waveform and any corresponding reflections). Examples are described herein using Wi-Fi as an illustrative example. However, the systems and techniques are not limited to Wi-Fi. For example, in some cases, the systems and techniques can be implemented using 5G / New Radio (NR), such as using millimeter wave (mm wave) technology. In some cases, the systems and techniques can be implemented using other wireless technologies, such as Bluetooth, Ultra Wide Band (UWB), etc.

[0027] In some aspects, a device may include a Wi-Fi interface configured to implement algorithms with varying levels of RF detection resolution based on the bandwidth of the transmitted RF signals, the number of spatial streams, the number of antennas configured to transmit the RF signals, the number of antennas configured to receive the RF signals, the number of spatial links (e.g., the number of spatial streams multiplied by the number of antennas configured to receive the RF signals), the sampling rate, or any combination thereof. For example, the Wi-Fi interface of the device may be configured to implement a low-resolution RF detection algorithm that consumes a small amount of power and can operate in the background when the device is in a “locked” state and / or a “sleep” mode. In some instances, the low-resolution RF detection algorithm may be used by the device as a coarse detection mechanism that can detect motion within a certain vicinity of the device. In some aspects, the low-resolution RF detection algorithm may be used as a trigger to initiate a facial recognition system of the device and may provide lower latency than existing triggers (e.g., device motion, tapping screen, alert, etc.). In some aspects, detecting motion by using a low-resolution RF detection algorithm may trigger the device to run a higher-resolution RF detection algorithm (e.g., a medium-resolution RF detection algorithm, a high-resolution RF detection algorithm, or other higher-resolution RF detection algorithm as described herein) before initiating facial recognition.

[0028] In some examples, the Wi-Fi interface of the device can be configured to implement a medium-resolution RF detection algorithm. The transmitted RF signal utilized for the medium-resolution RF detection algorithm can differ from the low-resolution RF detection algorithm by having a larger bandwidth, more spatial streams, more spatial links (e.g., more antennas configured to receive the RF signals and / or more spatial streams), a higher sampling rate (corresponding to a smaller sampling interval), or any combination thereof. In some cases, the medium-resolution RF detection algorithm can be used to detect the presence of a user's head (or other body part, such as face, eyes, etc.) as well as movement proximate to the device. In some examples, the medium-resolution RF detection algorithm can be invoked in response to detecting movement proximate to the device by using the low-resolution RF detection algorithm as described above. In some cases, the medium-resolution RF detection algorithm may focus its detection on the user's head by utilizing digital signal processing to filter out signals not reflected from the direction facing the device's screen. In some examples, the medium-resolution RF detection algorithm can also be used as a trigger to initiate the device's facial recognition system and can provide lower latency than existing triggers (e.g., device movement, touchscreen interaction, alerts, etc.). In some cases, detecting the presence of a user's head by using a medium-resolution RF detection algorithm may trigger the device to run a higher-resolution RF detection algorithm (e.g., a high-resolution RF detection algorithm or other higher-resolution RF detection algorithm as described herein) before initiating facial recognition.

[0029] In another example, the Wi-Fi interface of the device can be configured to implement a high-resolution RF detection algorithm. The transmitted RF signal utilized for the high-resolution RF detection algorithm can differ from the medium-resolution RF detection algorithm and the low-resolution RF detection algorithm by having a larger bandwidth, more spatial streams, more spatial links (e.g., more antennas configured to receive the RF signals and / or more spatial streams), a higher sampling rate, or any combination thereof. In some instances, the high-resolution RF detection algorithm can be used to detect the orientation of the user's head (e.g., whether the user is facing the phone or looking elsewhere), the presence of the user's head, and / or motion proximate to the device. In some examples, the high-resolution RF detection algorithm can be invoked in response to detecting motion proximate to the device and / or in response to detecting the presence of the user's head (or other body part, such as the face, eyes, etc.). In some aspects, the high-resolution RF detection algorithm may utilize digital signal processing to filter out signals not reflected from the direction facing the device's screen. In some cases, the high-resolution RF detection algorithm can be used as a trigger to initiate a device's facial recognition system and can provide lower latency than the existing facial recognition triggers described above.

[0030] In some examples, the Wi-Fi interface of the device can be configured to implement a facial recognition RF detection algorithm. In one implementation, the device may utilize an RF interface capable of transmitting extremely high frequency (EHF) signals or mm-wave technology (e.g., IEEE 802.11ad) to perform facial recognition. For example, the device may include an mm-wave RF interface. In some examples, the mm-wave RF interface can utilize one or more directional antennas configured to transmit signals in a direction perpendicular to the device screen. For example, the device can utilize the mm-wave RF interface to perform a narrow beam sweep to obtain time-of-flight and phase measurements at different angles from various signals reflected from the user's face. In some examples, the device can utilize the time-of-flight and phase measurements to generate a facial signature. The device can compare the facial signature to calibrated facial metrics stored in the system for facial recognition.

[0031] Facial recognition implementations using such systems and techniques can advantageously function in direct sunlight or for users wearing IR-blocking glasses. Furthermore, facial recognition using these systems and techniques can incorporate three-dimensional data about the user's face and therefore can provide greater accuracy than existing systems.

[0032] In some examples, systems and techniques can perform RF sensing associated with each of the above-mentioned algorithms by implementing a device's Wi-Fi interface with at least two antennas that can be used to simultaneously transmit and receive RF signals. In some cases, the antennas can be omnidirectional so that they can receive and transmit RF signals in all directions. For example, a device may utilize the device's Wi-Fi interface's transmitter to transmit an RF signal and simultaneously enable the Wi-Fi interface's Wi-Fi receiver so that the device may capture any signals reflected from the user. The Wi-Fi receiver can also be configured to detect leakage signals that are not reflected from objects and are transferred from the Wi-Fi transmitter's antenna to the Wi-Fi receiver's antenna. In doing so, the device may collect RF sensing data in the form of channel state information (CSI) data regarding the direct path (leakage signal) of the transmitted signal, along with data regarding the reflected path of the received signal corresponding to the transmitted signal.

[0033] In some aspects, the CSI data can be used to calculate the range and angle of arrival of the reflected signal. The range and angle of the reflected signal can be used to detect movement, determine the presence of a user's head, face, eyes, feet, hands, etc., and / or determine the orientation of the user's face as described above. In some examples, the range and angle of arrival of the reflected signal can be determined using signal processing, machine learning algorithms, or any other suitable technique, or any combination thereof. In one example, the range of the reflected signal can be calculated by measuring the time difference between receiving the leak signal and receiving the reflected signal. In another example, the angle of arrival can be calculated by utilizing an antenna array to receive the reflected signal and measuring the difference in reception phase at each element of the antenna array. In some cases, the range of the reflected signal, along with the angle of arrival of the reflected signal, can be used to identify the presence and orientation characteristics of a user, such as by identifying the presence and / or orientation of the user's head.

[0034] In some examples, one or more of the various RF detection algorithms described herein can be used to perform device management functions based on user awareness. For example, one or more of the RF detection algorithms can be used to determine the orientation of a user's head. The orientation of the user's head can then be used to infer whether the user is directing their attention toward the device screen or elsewhere. Such implementations can consume less power than existing systems that utilize facial recognition to confirm user awareness.

[0035] Various aspects of the systems and techniques described herein are discussed below with reference to the drawings. FIG. 1 illustrates an example of a computing system 170 of a user device 107. The user device 107 is an example of a device that can be used by an end user. For example, the user device 107 can include a mobile phone, a router, a tablet computer, a laptop computer, a tracking device, a wearable device (e.g., a smart watch, glasses, an XR device, etc.), an Internet of Things (IoT) device, a vehicle (or a computing device in the vehicle), and / or other device used by a user to communicate over a wireless communication network. In some cases, a device can be referred to as a station (STA), such as when referring to a device configured to communicate using the Wi-Fi standard. In some cases, a device can be referred to as user equipment (UE), such as when referring to a device configured to communicate using 5G / New Radio (NR), Long Term Evolution (LTE), or other telecommunications standards.

[0036] Computing system 170 includes software and hardware components that may be electrically or communicatively coupled (or otherwise in communication as appropriate) via a bus 189. For example, computing system 170 includes one or more processors 184. The one or more processors 184 may include one or more CPUs, ASICs, FPGAs, APs, GPUs, VPUs, NSPs, microcontrollers, special purpose hardware, any combination thereof, and / or other processing devices and / or systems. Bus 189 may be used by one or more processors 184 for communication between cores and / or with one or more memory devices 186.

[0037] The computing system 170 may also include one or more memory devices 186, one or more digital signal processors (DSPs) 182, one or more subscriber identity modules (SIMs) 174, one or more modems 176, one or more wireless transceivers 178, one or more antennas 187, one or more input devices 172 (e.g., a camera, a mouse, a keyboard, a touch-sensitive screen, a touchpad, a keypad, a microphone, etc.), and one or more output devices 180 (e.g., a display, speakers, a printer, etc.).

[0038] One or more wireless transceivers 178 may receive wireless signals (e.g., signals 188) via antenna 187 from one or more other devices, such as a user device, a network device (e.g., a base station such as an eNB and / or gNB, a Wi-Fi access point (AP) such as a router, a range extender, etc.), a cloud network, etc. In some examples, computing system 170 may include multiple antennas or an antenna array that can facilitate simultaneous transmit and receive capabilities. In some examples, antenna 187 may be an omnidirectional antenna so as to be able to receive and transmit RF signals in all directions. Wireless signals 188 may be transmitted over a wireless network. The wireless network may be any wireless network, such as a cellular or telecommunications network (e.g., 3G, 4G, 5G, etc.), a wireless local area network (e.g., a WiFi network), a Bluetooth™ network, and / or other network. In some examples, the one or more wireless transceivers 178 may include an RF front end that includes one or more components such as an amplifier, a mixer (also called a signal multiplier) for signal downconversion, a frequency synthesizer (also called an oscillator) that provides a signal to the mixer, a baseband filter, an analog-to-digital converter (ADC), one or more power amplifiers, etc. The RF front end generally can handle the selection and conversion of the wireless signal 188 to a baseband frequency or an intermediate frequency and can convert the RF signal to the digital domain, among other components.

[0039] In some cases, computing system 170 may include a coding-decoding device (or codec) configured to encode and / or decode data transmitted and / or received using one or more wireless transceivers 178. In some cases, computing system 170 may include an encryption-decryption device or component configured to encrypt and / or decrypt data transmitted and / or received by one or more wireless transceivers 178 (e.g., in accordance with the Advanced Encryption Standard (AES) and / or Data Encryption Standard (DES) standards).

[0040] Each of the one or more SIMs 174 can securely store an International Mobile Subscriber Identity (IMSI) number and associated keys assigned to a user of the user device 107. The IMSI and keys can be used to identify and authenticate a subscriber when accessing a network provided by a network service provider or operator associated with the one or more SIMs 174. The one or more modems 176 can modulate one or more signals to encode information for transmission using one or more wireless transceivers 178. The one or more modems 176 can also demodulate signals received by the one or more wireless transceivers 178 to decode transmitted information. In some examples, the one or more modems 176 can include a WiFi modem, a 4G (or LTE) modem, a 5G (or NR) modem, and / or other types of modems. The one or more modems 176 and the one or more wireless transceivers 178 can be used to communicate data for the one or more SIMs 174.

[0041] Computing system 170 may also include (and / or be in communication with) one or more non-transitory machine-readable storage media or devices (e.g., one or more memory devices 186), which may include, but are not limited to, local and / or network-accessible storage, disk drives, drive arrays, optical storage devices, solid-state storage devices such as RAM and / or ROM that may be programmable, flash-updateable, etc. Such storage devices may be configured to implement any suitable data storage, including, but not limited to, various file systems, database structures, etc.

[0042] In various embodiments, the functions may be stored in memory device 186 as one or more computer program products (e.g., instructions or code) and executed by one or more processors 184 and / or one or more DSPs 182. Computing system 170 may also include software elements (e.g., located in one or more memory devices 186) including, for example, an operating system, device drivers, executable libraries, and / or other code, such as one or more application programs, which may comprise computer programs that perform the functions provided by various embodiments and / or may be designed to implement the methods and / or configure the systems described herein.

[0043] 2 is a diagram illustrating an example of a wireless device 200 that utilizes RF sensing techniques to perform one or more functions, such as detecting the presence of a user 202, detecting orientation characteristics of the user, performing facial recognition, or any combination thereof, and / or to perform other functions. In some examples, the wireless device 200 may be a user device 107, such as a mobile phone, a tablet computer, a wearable device, or other device that includes at least one RF interface. In some examples, the wireless device 200 may be a device that provides connectivity for a user device (e.g., for the user device 107), such as a wireless access point (AP), a base station (e.g., gNB, eNB, etc.), or other device that includes at least one RF interface.

[0044] In some aspects, wireless device 200 may include one or more components for transmitting RF signals. Wireless device 200 may include a digital-to-analog converter (DAC) 204, which may receive a digital signal or waveform (e.g., from a microprocessor, not shown) and convert the signal or waveform to an analog waveform. The analog signal output by DAC 204 may be provided to RF transmitter 206. RF transmitter 206 may be a Wi-Fi transmitter, a 5G / NR transmitter, a Bluetooth™ transmitter, or any other transmitter capable of transmitting RF signals.

[0045] The RF transmitter 206 may be coupled to one or more transmit antennas, such as a TX antenna 212. In some examples, the TX antenna 212 may be an omnidirectional antenna capable of transmitting RF signals in all directions. For example, the TX antenna 212 may be an omnidirectional Wi-Fi antenna capable of radiating Wi-Fi signals (e.g., 2.4 GHz, 5 GHz, 6 GHz, etc.) in a 360-degree radiation pattern. In another example, the TX antenna 212 may be a directional antenna that transmits RF signals in a specific direction.

[0046] In some examples, the wireless device 200 may also include one or more components for receiving RF signals. For example, the receiver lineup in the wireless device 200 may include one or more receive antennas, such as the RX antenna 214. In some examples, the RX antenna 214 may be an omnidirectional antenna capable of receiving RF signals in multiple directions. In other examples, the RX antenna 214 may be a directional antenna configured to receive signals from a particular direction. In further examples, the TX antenna 212 and the RX antenna 214 may both include multiple antennas (e.g., elements) configured as an antenna array.

[0047] The wireless device 200 may also include an RF receiver 210 coupled to an RX antenna 214. The RF receiver 210 may include one or more hardware components for receiving an RF waveform, such as a Wi-Fi signal, a Bluetooth™ signal, a 5G / NR signal, or any other RF signal. An output of the RF receiver 210 may be coupled to an analog-to-digital converter (ADC) 208. The ADC 208 may be configured to convert the received analog RF waveform into a digital waveform that can be provided to a processor, such as a digital signal processor (not shown).

[0048] In one example, the wireless device 200 may implement an RF sensing technique by transmitting a TX waveform 216 from the TX antenna 212. While the TX waveform 216 is illustrated as a single line, in some cases the TX waveform 216 may be transmitted in all directions by the omni-directional TX antenna 212. In one example, the TX waveform 216 may be a Wi-Fi waveform transmitted by a Wi-Fi transmitter in the wireless device 200. In some cases, the TX waveform 216 may correspond to a Wi-Fi waveform transmitted simultaneously or nearly simultaneously with a Wi-Fi data communication signal or a Wi-Fi control function signal (e.g., a beacon transmission). In some examples, the TX waveform 216 may be transmitted using the same or similar frequency resources as the Wi-Fi data communication signal or the Wi-Fi control function signal (e.g., a beacon transmission). In some aspects, the TX waveform 216 may correspond to a Wi-Fi waveform transmitted separately from the Wi-Fi data communication signal and / or the Wi-Fi control signal (e.g., the TX waveform 216 may be transmitted at different times and / or using different frequency resources).

[0049] In some examples, the TX waveform 216 can correspond to a 5G NR waveform transmitted simultaneously or nearly simultaneously with a 5G NR data communication signal or a 5G NR control function signal. In some examples, the TX waveform 216 can be transmitted using the same or similar frequency resources as the 5G NR data communication signal or the 5G NR control function signal. In some aspects, the TX waveform 216 can correspond to a 5G NR waveform transmitted separately from the 5G NR data communication signal and / or the 5G NR control signal (e.g., the TX waveform 216 can be transmitted at different times and / or using different frequency resources).

[0050] In some aspects, one or more parameters associated with the TX waveform 216 can be modified to be used to increase or decrease the RF sensing resolution. The parameters may include frequency, bandwidth, number of spatial streams, number of antennas configured to transmit the TX waveform 216, number of antennas configured to receive reflected RF signals corresponding to the TX waveform 216, number of spatial links (e.g., number of spatial streams multiplied by number of antennas configured to receive the RF signals), sampling rate, or any combination thereof.

[0051] In a further example, the TX waveform 216 can be implemented to have a sequence with perfect or nearly perfect autocorrection properties. For example, the TX waveform 216 can include a single-carrier Zadoff sequence or can include symbols similar to orthogonal frequency division multiplexing (OFDM) long training field (LTF) symbols. In some cases, the TX waveform 216 can include a chirp signal, such as those used in frequency-modulated continuous wave (FM-CW) radar systems. In some configurations, the chirp signal can include a signal in which the signal frequency increases and / or decreases linearly and / or exponentially.

[0052] In some aspects, the wireless device 200 may further implement RF sensing techniques by performing simultaneous transmit and receive functions. For example, the wireless device 200 may enable its RF receiver 210 to receive simultaneously or nearly simultaneously as it enables the RF transmitter 206 to transmit the TX waveform 216. In some examples, the transmission of a sequence or pattern included in the TX waveform 216 may be continuously repeated such that the sequence is transmitted a certain number of times or for a certain duration. In some examples, the repetition of a pattern in the transmission of the TX waveform 216 may be used to avoid missing reception of a reflected signal if the RF receiver 210 is enabled after the RF transmitter 206. In one example implementation, the TX waveform 216 may include a sequence having a sequence length L that is transmitted more than once, thereby enabling the RF receiver 210 in a time equal to or less than L to receive a reflection corresponding to the entire sequence without losing information.

[0053] The wireless device 200 can receive a signal corresponding to the TX waveform 216 by implementing simultaneous transmit and receive functionality. For example, the wireless device 200 can receive a signal reflected from an object or person within range of the TX waveform 216, such as the RX waveform 218 reflected from the user 202. The wireless device 200 can also receive a leakage signal (e.g., a TX leakage signal 220) that is directly coupled from the TX antenna 212 to the RX antenna 214 without being reflected from an object. For example, the leakage signal can include a signal that is transferred from a transmit antenna (e.g., the TX antenna 212) on the wireless device to a receive antenna (e.g., the RX antenna 214) on the wireless device without being reflected from an object. In some cases, the RX waveform 218 can include multiple sequences corresponding to multiple copies of a sequence included in the TX waveform 216. In some examples, the wireless device 200 can combine multiple sequences received by the RF receiver 210 to improve the signal-to-noise ratio (SNR).

[0054] The wireless device 200 may further implement an RF sensing technique by obtaining RF sensing data associated with each received signal corresponding to the TX waveform 216. In some examples, the RF sensing data may include channel state information (CSI) data regarding the direct path of the TX waveform 216 (e.g., the leakage signal 220) along with data regarding the reflected path corresponding to the TX waveform 216 (e.g., the RX waveform 218).

[0055] In some aspects, the RF sensing data (e.g., CSI data) may include information that can be used to determine how an RF signal (e.g., the TX waveform 216) propagates from the RF transmitter 206 to the RF receiver 210. The RF sensing data may include data corresponding to effects on the transmitted RF signal due to scattering, fading, and power attenuation over distance, or any combination thereof. In some examples, the RF sensing data may include imaginary and real data (e.g., I / Q components) corresponding to each tone in the frequency domain over a particular bandwidth.

[0056] In some examples, the RF sensing data can be used to calculate the distance and angle of arrival corresponding to a reflected waveform, such as RX waveform 218. In further examples, the RF sensing data can also be used to detect motion, determine location, detect changes in location or motion patterns, obtain channel estimates, or any combination thereof. In some cases, the distance and angle of arrival of the reflected signal can be used to identify the size, location, movement, or orientation of a user (e.g., user 202) within the surrounding environment for detecting user presence / proximity, detecting user attention, and / or performing facial recognition and user authentication (e.g., face authentication).

[0057] The wireless device 200 may utilize signal processing, machine learning algorithms, or any other suitable technique to calculate the distance and angle of arrival corresponding to the reflected waveform (e.g., the distance and angle of arrival corresponding to the RX waveform 218) by any combination thereof. In other examples, the wireless device 200 may transmit the RF sensing data to another computing device, such as a server, which may perform calculations to obtain the distance and angle of arrival corresponding to the RX waveform 218 or other reflected waveforms.

[0058] In one example, the distance of the RX waveform 218 can be calculated by measuring the time difference between receiving the leak signal and receiving the reflected signal. For example, the wireless device 200 can determine a zero baseline distance based on the difference (e.g., propagation delay) between the time the wireless device 200 transmits the TX waveform 216 and the time it receives the leak signal 220. The wireless device 200 can then determine the distance associated with the RX waveform 218 based on the difference (e.g., time of flight) between the time the wireless device 200 transmits the TX waveform 216 and the time it receives the RX waveform 218, and can adjust this difference depending on the propagation delay associated with the leak signal 220. In doing so, the wireless device 200 can determine the distance traveled by the RX waveform 218, which can be used to determine the presence and movement of a user (e.g., user 202) that caused the reflection.

[0059] In a further example, the angle of arrival of the RX waveform 218 can be calculated by measuring the time difference of arrival of the RX waveform 218 between individual elements of a receive antenna array, such as antenna 214. In some examples, the time difference of arrival can be calculated by measuring the difference in receive phase at each element in the receive antenna array.

[0060] In some cases, the distance and angle of arrival of the RX waveforms 218 can be used to determine the distance between the wireless device 200 and the user 202 as well as the location of the user 202 relative to the wireless device 200. The distance and angle of arrival of the RX waveforms 218 can also be used to determine the presence, movement, proximity, attention, identification, or any combination thereof, of the user 202. For example, the wireless device 200 can utilize the calculated distance and angle of arrival corresponding to the RX waveforms 218 to determine that the user 202 is walking towards the wireless device 200. Based on the proximity of the user 202 to the wireless device 200, the wireless device 200 can activate facial recognition to unlock the device. In some aspects, facial recognition can be activated based on the user 202 being within a threshold distance of the wireless device 200. Examples of the threshold distance can include 2 feet, 1 foot, 6 inches, 3 inches, or any other distance.

[0061] As mentioned above, wireless device 200 may include a mobile device (e.g., a smartphone, a laptop, a tablet, etc.) or other type of device. In some examples, wireless device 200 may be configured to acquire device location data and device orientation data along with RF detection data. In some instances, the device location data and device orientation data may be used to determine or adjust the distance and angle of arrival of a reflected signal, such as RX waveform 218. For example, wireless device 200 may be set on a table facing a ceiling as user 202 walks toward wireless device 200 during an RF detection process. In this example, wireless device 200 may use its location data and orientation data along with the RF detection data to determine the direction in which user 202 is walking.

[0062] In some examples, device location data may be collected by wireless device 200 using techniques including round-trip time (RTT) measurements, passive positioning, angle of arrival, received signal strength indicator (RSSI), CSI data, using any other suitable technique, or any combination thereof. In a further example, device orientation data may be obtained from electronic sensors on wireless device 200, such as a gyroscope, accelerometer, compass, magnetometer, barometer, any other suitable sensor, or any combination thereof.

[0063] 3 is a diagram illustrating an environment 300 including a wireless device 302, an access point (AP) 304, and a user 308. The wireless device 302 may include a user device (e.g., user device 107 of FIG. 1, such as a mobile device or any other type of device). The AP 304 may also be referred to as a wireless device in some examples. As shown, the user 308 may move to different locations (e.g., with the wireless device 302), including a first user location 309a, a second user location 309b, and a third user location 309c. In some aspects, each of the wireless device 302 and the AP 304 may be configured to perform RF sensing to detect the presence of the user 308, detect movement of the user 308, perform facial recognition of the user 308, or any combination thereof, and / or perform other functions for the user 308.

[0064] In some aspects, the AP 304 may be a Wi-Fi access point that includes hardware and software components that may be configured to simultaneously transmit and receive RF signals, such as the components described herein with respect to the wireless device 200 of FIG. 2. For example, the AP 304 may include one or more antennas that may be configured to transmit RF signals and one or more antennas that may be configured to receive RF signals (e.g., antenna 306). As noted with respect to the wireless device 200 of FIG. 2, the AP 304 may include an omni-directional antenna or antenna array configured to transmit and receive signals from any direction.

[0065] In some aspects, the AP 304 and the wireless device 302 can be configured to implement a bistatic configuration in which the transmit and receive functions are performed by different devices. For example, the AP 304 can transmit an omnidirectional RF signal that can include signal 310a and signal 310b. As shown, signal 310a can travel directly (e.g., without reflection) from the AP 304 to the wireless device 302, and signal 310b can be reflected from the user 308 at location 309a, causing a corresponding reflected signal 312 to be received by the wireless device 302.

[0066] In some examples, the wireless device 302 may utilize RF sensing data associated with the signals 310a and 310b to determine the presence, location, orientation, and / or movement of the user 308 at the location 309a. For example, the wireless device 302 may acquire, retrieve, and / or estimate location data associated with the AP 304. In some aspects, the wireless device 302 may use the location data and RF sensing data (e.g., CSI data) associated with the AP 304 to determine time-of-flight, distance, and / or angle-of-arrival related signals transmitted by the AP 304 (e.g., direct path signals such as signal 310a and reflected path signals such as signal 312). In some cases, the AP 304 and the wireless device 302 may further transmit and / or receive communications that may include data associated with the RF signal 310a and / or reflected signal 312 (e.g., transmission time, sequence / pattern, time of arrival, angle of arrival, etc.).

[0067] In some examples, the wireless device 302 may be configured to perform RF sensing using a monostatic configuration, in which the wireless device 302 performs both transmit and receive functions (e.g., simultaneous TX / RX as described in connection with the wireless device 200). For example, the wireless device 302 may detect the presence or movement of a user 308 at location 309b by transmitting an RF signal 314, which may cause a reflected signal 316 from the user 308 at location 309b to be received by the wireless device 302.

[0068] In some aspects, the wireless device 302 may obtain RF sensing data related to the reflected signal 316. For example, the RF sensing data may include CSI data corresponding to the reflected signal 316. In a further aspect, the wireless device 302 may use the RF sensing data to calculate a distance and an angle of arrival corresponding to the reflected signal 316. For example, the wireless device 302 may determine the distance by calculating a time of flight for the reflected signal 316 based on the difference between a leak signal (not shown) and the reflected signal 316. In a further example, the wireless device 302 may utilize an antenna array to receive the reflected signal and determine the angle of arrival by measuring the difference in receive phase at each element of the antenna array.

[0069] In some examples, the wireless device 302 may obtain RF sensing data in the form of CSI data that can be used to formulate a matrix based on a number of frequencies, represented as 'K' (e.g., tones), and a number of antenna array elements, represented as 'N'. In one technique, the CSI matrix may be formulated according to the relationship given by mathematical equation (1). CSI matrix: H = [h ik ], i = 1,…, N, k = 1,…, K (1)

[0070] Once the wireless device 302 has formulated the CSI matrix, it can calculate the angle of arrival and time of flight for the direct signal path (e.g., the leakage signal) and the reflected signal path (e.g., the reflected signal 316) by utilizing a two-dimensional Fourier transform. In one example, the Fourier transform can be defined by the relationship given by the following equation (2), where K corresponds to the number of tones in the frequency domain, N corresponds to the number of receive antennas, and h ikwhere ℓ corresponds to the CSI data captured on the i-th antenna and k-th tone (e.g., a complex number having real and imaginary values), f corresponds to the carrier frequency, l corresponds to the antenna spacing, c corresponds to the speed of light, and Δf corresponds to the frequency spacing between two adjacent tones. The relationship in Equation (2) is given as follows:

[0071]

number

[0072] In some aspects, the leak signal (eg, leak signal 220 and / or other leak signals) may be cancelled by using an iterative cancellation method.

[0073] In some cases, the wireless device 302 can utilize the distance and angle of arrival corresponding to the reflected signal 316 to detect the presence or movement of the user 308 at location 309b. In other examples, the wireless device 302 can detect further movement of the user 308 to a third location 309c. The wireless device 302 can transmit an RF signal 318 that causes a reflected signal 320 from the user 308 at location 309c. Based on the RF sensing data associated with the reflected signal 320, the wireless device 302 can determine the presence of the user 308 at location 309c, detect the presence and / or orientation of the user's head, and perform facial recognition and authentication.

[0074] In some implementations, the wireless device 302 may utilize artificial intelligence or machine learning algorithms to perform motion detection, object classification, and / or detect head orientation with respect to the user 308. In some examples, the machine learning techniques may include supervised machine learning techniques utilizing neural networks, linear and logistic regression, classification trees, support vector machines, any other suitable supervised machine learning techniques, or any combination thereof. For example, a data set of sample RF sensing data may be selected for training the machine learning algorithm or artificial intelligence.

[0075] In some aspects, the wireless device 302 and the AP 304 may perform RF sensing techniques regardless of their association with each other or with a Wi-Fi network. For example, the wireless device 302 may utilize its Wi-Fi transmitter and Wi-Fi receiver to perform RF sensing as described herein when not associated with an access point or a Wi-Fi network. In a further example, the AP 304 may perform RF sensing techniques regardless of whether the wireless device is associated.

[0076] 4 is a flow diagram illustrating an example process 400 for performing facial recognition. Block 402 illustrates a device state where the device is locked and the device's screen (or display) is off. For example, a user may have the device on a desk and the device may transition to a locked state after a period of inactivity.

[0077] At block 404, the device may perform RF sensing to detect motion proximate the device. In one example, RF sensing to detect motion may be implemented by configuring an RF interface on the device to simultaneously perform transmit and receive functions (similar to the description above for wireless device 200 of FIG. 2). For example, a Wi-Fi interface on the device may be configured to transmit one or more RF signals and simultaneously (or nearly simultaneously) receive one or more reflected signals corresponding to the transmitted RF signals.

[0078] In some implementations, a device may be configured to implement RF detection algorithms with varying levels of RF detection resolution based on parameters such as the bandwidth of the transmitted RF signals, the number of spatial streams, the number of antennas configured to transmit the RF signals, the number of antennas configured to receive the RF signals, the number of spatial links (e.g., the number of spatial streams multiplied by the number of antennas configured to receive the RF signals), the sampling rate, or any combination thereof. For example, a device may implement an algorithm that can detect motion proximate to the device when the device is in a locked or sleep state by adjusting one or more parameters related to the bandwidth, sampling rate, and / or spatial links.

[0079] For example, in some cases, a device may be configured to transmit using spatial streaming or multiplexing techniques that can be used to transmit independently and separately coded signals (e.g., streams) from each of the transmit antennas. For example, a wireless device with four antennas may be configured to implement a 1×3 configuration (e.g., one spatial stream and three RX antennas resulting in three spatial links) by configuring one antenna for transmission and the remaining three antennas for reception (e.g., in which case one TX antenna may transmit a spatial stream that can be received by the other three RX antennas). In another example, a wireless device may implement a 2×2 configuration (e.g., two spatial streams and two RX antennas resulting in four spatial links) by transmitting independent signals via two antennas configured for transmission that can be received by the two antennas configured for reception.

[0080] In some configurations, a device can adjust the level of RF detection resolution by modifying the number of spatial links (e.g., adjusting the number of spatial streams and / or the number of receive antennas) as well as the bandwidth and sampling frequency. In some cases, a device can implement a low-resolution RF detection algorithm (e.g., using a relatively low bandwidth, a small number of spatial links, and a low sampling rate), which consumes a small amount of power and can operate in the background while the device is locked or asleep. In one illustrative example, a device can perform motion detection by configuring its RF interface to transmit a signal with a bandwidth of approximately 20 MHz using a single spatial link and utilizing a sampling rate that can range from 100 ms to 500 ms. Those skilled in the art will understand that the parameters and corresponding values ​​described herein are provided as example configurations and that the disclosed systems and techniques may be implemented using different variations of the parameters and values.

[0081] At block 406, the device may determine whether motion is detected based on the RF sensing data. The device may detect motion by utilizing signal processing, machine learning algorithms, the use of any other suitable techniques, or any combination thereof. If motion is not detected, process 400 may proceed to block 408, where the device remains locked and continues to perform RF sensing to detect motion. In such a case, the device may continue to perform RF sensing using a low-resolution RF sensing algorithm.

[0082] If motion is detected at block 406, process 400 may proceed to block 410 and begin facial authentication. In some examples, facial recognition may be performed by using an RF interface capable of transmitting extremely high frequency (EHF) signals or mm-wave technology (e.g., IEEE 802.11ad), such as transmitting signals in a direction perpendicular to the device screen. For example, the device may utilize the mm-wave RF interface to perform narrow beam sweeps to obtain time-of-flight and phase measurements at different angles from signals reflected from the user's face. In some examples, the device may utilize the time-of-flight and phase measurements to generate a facial signature that the device can compare to calibrated facial metrics stored in the system for facial recognition.

[0083] In another example, a device may use light detection and ranging (LIDAR) to perform facial recognition. For example, a LIDAR device can be used to shine a laser light at a user's face and measure the time it takes for the laser light to reflect back to the sensor. In some cases, the device can use the difference in return time and / or wavelength to generate a three-dimensional representation or image of the user's face, which can be used to perform facial recognition.

[0084] In another example, the device may use an infrared (IR) light source, dot projector, or other light source to illuminate the user's face and may use an IR camera or other image capture device to perform image capture. The captured image may then be processed and used to perform facial authentication in block 410. For example, the captured image or captured image data may be compared to a stored / enrolled face or corresponding signature to perform authentication. If process 400 determines that facial authentication failed in block 412, it may proceed to block 408, where the device continues to perform RF sensing to detect motion. If facial authentication is verified in block 412, process 400 proceeds to block 414, where the device is unlocked.

[0085] 5 is a flow diagram illustrating an example of a process 500 for performing facial recognition. Block 502 illustrates a device state in which the device is locked and the device's screen (or display) is off. For example, a user may have the device on a desk and / or the device may have entered a locked state after a period of inactivity.

[0086] At block 504, the device may perform RF sensing to detect the presence of a user in the vicinity of or within a threshold distance of the device. In one example, detecting the presence of a user may include detecting and identifying the presence of the user's head. In some implementations, RF sensing to detect the presence of a user may be performed in response to detecting motion, as described with respect to process 400. For example, the device may perform RF sensing by using parameters (e.g., bandwidth, sampling rate, spatial stream, spatial link, any combination thereof, and / or other parameters) that enable low-power operation for detecting motion. In response to detecting motion, the device may perform RF sensing using different sets of parameters (e.g., different bandwidths, different sampling rates, different spatial streams, different spatial links, etc.) that can be configured to detect the presence of a user.

[0087] A device placed on a desk in a room may use the techniques described with respect to process 400 to detect when a user is walking around the room but is not yet in close proximity to the device (e.g., not within a threshold distance where the user might operate the device). Once the device detects that the user is moving towards the device, it may implement a different RF sensing algorithm (e.g., according to process 500 of FIG. 5) that can be configured to detect the presence of the user by detecting and identifying the user's head.

[0088] RF sensing to detect the presence of a user can be implemented by configuring an RF interface on the device to simultaneously perform transmit and receive functions. For example, the RF interface on the device can be configured to transmit one or more RF signals and simultaneously, or nearly simultaneously, receive one or more reflected signals corresponding to the transmitted RF signals.

[0089] As mentioned above, in some implementations, a device can be configured to implement RF detection algorithms with various levels of RF detection resolution based on parameters such as the bandwidth of the transmitted RF signal, the number of spatial streams, the number of spatial links, the sampling rate, or any combination thereof. For example, a device may implement an algorithm that can detect the presence of a user when the device is in a locked or sleep state (e.g., by performing head detection to detect the presence of a head) by adjusting one or more parameters related to the bandwidth, sampling rate, and / or spatial link. In some cases, a device can implement a medium-resolution RF detection algorithm (e.g., having a medium bandwidth, a medium number of spatial links, and a medium sampling rate compared to a low-resolution RF detection algorithm), which can differ from a low-resolution RF detection algorithm by having a higher bandwidth, a higher number of spatial links, a higher sampling rate, or any combination thereof. For example, a device can increase the bandwidth, increase the sampling rate (collect more samples), and / or increase the number of spatial links to detect the presence of a user compared to the parameters used in process 400 of FIG. 4 to detect motion. In one illustrative example, the device may detect the presence of a user (e.g., head detection) by configuring the RF interface to transmit a signal with a bandwidth of approximately 40 MHz using two spatial links and utilizing a sampling rate that may be approximately 50 ms. As described with respect to process 400, those skilled in the art will understand that the parameters and corresponding values ​​described herein are provided as an example configuration and that the disclosed systems and techniques may be implemented using different variations of the parameters and values.

[0090] At block 506, the device may determine whether a user's presence is detected based on the RF detection data. The device may detect a user's presence by utilizing signal processing, machine learning algorithms, the use of any other suitable techniques, or any combination thereof. If a user's presence is not detected (e.g., a head is not detected), process 500 may proceed to block 508, and the device remains locked and continues to perform RF detection to detect a user's presence (e.g., a head presence) using process 500 of FIG. 5 or to detect movement using process 400 of FIG. 4. In such a case, the device may continue to perform RF detection using a medium-resolution RF detection algorithm.

[0091] If a user's presence is detected at block 506, process 500 may proceed to block 510 and begin facial authentication. In some examples, facial authentication may be performed using RF sensing techniques, using the device's IR camera to capture images, using a LIDAR sensor, or any combination thereof. Further details regarding facial authentication are provided in the description of FIG. 8.

[0092] If facial authentication fails at block 512, process 500 may proceed to block 508, where the device continues to perform RF detection to detect the presence of a user. In some configurations, the process may alternatively perform RF detection to detect motion, as described in connection with process 400. If facial authentication is verified at block 512, process 500 proceeds to block 514, where the device is unlocked.

[0093] 6 is a flow diagram illustrating an example of a process 600 for performing facial recognition. Block 602 illustrates a device state in which the device is locked and the device's screen (or display) is off. Similar to the examples from FIGS. 4 and 5, a user may have the device on a desk and / or the device may have entered a locked state after a period of inactivity.

[0094] At block 604, the device may perform RF sensing to determine whether a user is facing the device (e.g., based on head orientation). In some implementations, RF sensing may be performed to determine the user's head orientation in response to detecting the user's head, as described with respect to process 500. For example, the device may perform RF sensing by using parameters (e.g., bandwidth, sampling rate, spatial streams, spatial links, any combination thereof, and / or other parameters) configured to detect the presence of a user (e.g., head presence) while minimizing power consumption. The device may perform RF sensing using a different set of parameters (e.g., bandwidth, sampling rate, spatial streams, spatial links, any combination thereof, and / or other parameters) that can be configured to determine the user's head orientation in response to detecting the user's presence.

[0095] In one example, a device placed on a desk in a room may use the techniques described with respect to process 500 of FIG. 5 to determine that a user's head is in close proximity to the device. In some aspects, the user's head is in close proximity to the device if it is within a threshold distance (e.g., 2 feet) of the device. When the user is present and the head is detected within the threshold distance, RF detection techniques may then be triggered to determine the orientation of the user's head. For example, RF detection may determine whether the user is facing the device or if the device is in the user's lap while the user's attention is directed elsewhere.

[0096] RF sensing for detecting head orientation can be implemented by configuring an RF interface on the device to simultaneously perform transmit and receive functions. For example, the RF interface on the device can be configured to transmit one or more RF signals and simultaneously, or nearly simultaneously, receive one or more reflected signals corresponding to the transmitted RF signals.

[0097] As described above, a device can be configured to implement RF detection algorithms with various levels of RF detection resolution based on parameters such as the bandwidth of the transmitted RF signal, the number of spatial streams, the number of spatial links, the sampling rate, or any combination thereof. For example, a device may implement an algorithm that can detect the orientation of a user's head when the device is in a locked or sleep state by adjusting one or more parameters related to the bandwidth, sampling rate, and / or spatial links. In some cases, a device can implement a high-resolution RF detection algorithm (e.g., having a larger bandwidth, more spatial links, and a higher sampling rate compared to a medium-resolution RF detection algorithm). The high-resolution RF detection algorithm can differ from a medium-resolution RF detection algorithm by having a larger bandwidth, more spatial links, a higher sampling rate, or any combination thereof. For example, a device can increase the bandwidth, increase the sampling rate (collect more samples), and / or increase the number of spatial links to detect the orientation of a head compared to the parameters used in process 500 of FIG. 5 to detect the presence of a head. In one illustrative example, a device can detect head orientation by configuring the RF interface to transmit signals with a bandwidth of 80-160 MHz using three or more spatial links and utilizing a sampling rate of less than 50 ms. As described with respect to the process above, those skilled in the art will understand that the parameters and corresponding values ​​described herein are provided as an example configuration and that the disclosed systems and techniques may be implemented using different variations of the parameters and values.

[0098] At block 606, the device may determine whether the user's head is pointed toward the device based on the RF detection data. The device may detect the user's head orientation by utilizing signal processing, machine learning algorithms, the use of any other suitable techniques, or any combination thereof. If the device determines that the user's head is not pointed toward the device, process 600 may proceed to block 608, and the device remains locked and continues to perform RF detection to detect head orientation using process 600 of FIG. 6, to detect user presence (e.g., head presence) using process 500 of FIG. 5, or to detect movement using process 400 of FIG. 4. In such a case, the device may continue to perform RF detection using a high-resolution RF detection algorithm.

[0099] In some examples, the device may determine that the user is pointing toward another device if it determines in block 606 that the user's head is not pointing toward the device. In some aspects, the device may implement a positioning algorithm (e.g., round-trip time (RTT) measurements, passive positioning, angle of arrival, received signal strength indicator (RSSI), CSI data, use of any other suitable technique, or any combination thereof), or the device may access location data (e.g., already stored location data or location data from a server) to determine the range or distance to the other device. In some cases, the device may use location data along with device orientation data (e.g., data from a gyroscope) to determine that the user's head is pointing toward another device. In one illustrative example, the device may determine that the user is pointing toward a television, and the device may communicate with the television to turn on the television.

[0100] If, at block 606, the device determines that the user's head is pointed toward the device, process 600 may proceed to block 610 and begin facial authentication. In some examples, facial authentication may be performed using RF sensing techniques, using the device's IR camera to capture images, using a LIDAR sensor, or any combination thereof. Further details regarding facial authentication are provided in the description of FIG. 8.

[0101] If facial authentication fails at block 612, process 600 may proceed to block 608, where the device continues to perform RF detection to detect the orientation of the user's head. In some configurations, the process may alternatively perform RF detection to detect motion or user presence, as described in connection with processes 400 and 500, respectively. If facial authentication is verified at block 612, process 600 proceeds to block 614, where the device is unlocked.

[0102] 7 is a flow diagram illustrating an example of a process 700 for performing facial recognition. Block 702 illustrates a device state where the device is locked and the device's screen (or display) is off. Similar to the examples from FIGS. 4, 5, and 6, a user may have the device on a desk and / or the device may have entered a locked state after a period of inactivity.

[0103] At block 704, the device may execute a low-resolution RF detection algorithm to detect motion within the device's vicinity (e.g., detect a user walking in the same room as the device). In one example, RF detection to detect motion may be implemented by configuring an RF interface on the device to simultaneously perform transmit and receive functions (similar to the description above for wireless device 200 of FIG. 2). For example, a Wi-Fi interface on the device may be configured to transmit one or more RF signals and simultaneously (or nearly simultaneously) receive one or more reflected signals corresponding to the transmitted RF signals. In one illustrative example, the low-resolution RF detection algorithm may be implemented by configuring the RF interface to transmit signals having a bandwidth of approximately 20 MHz utilizing a single spatial link and utilizing a sampling rate that may range from 100 ms to 500 ms.

[0104] At block 706, the device may determine whether motion is detected based on the RF sensing data. If motion is not detected, process 700 may return to block 704, where the device remains in a locked state and continues to perform low-resolution RF sensing to detect motion.

[0105] If motion is detected at block 706, process 700 may proceed to block 708, where the device may execute a medium-resolution RF detection algorithm to detect the presence of a user near or within a threshold distance of the device. In one example, detecting the presence of a user may include detecting and identifying the presence of the user's head. The medium-resolution RF detection algorithm may differ from the low-resolution RF detection algorithm by having a larger bandwidth, more spatial links, a higher sampling rate, or any combination thereof. In one illustrative example, the device may detect the presence of a user (e.g., head detection) by configuring the RF interface to transmit a signal with a bandwidth of approximately 40 MHz utilizing two spatial links and utilizing a sampling rate that may be approximately 50 ms.

[0106] At block 710, the device may determine whether a user presence is detected based on the RF detection data. If a user presence is not detected, process 700 may continue to perform a medium resolution RF detection algorithm at block 712. In some examples, the medium resolution RF detection algorithm may be used to detect motion at block 706. If motion is not detected, process 700 may return to block 704, where the device remains in a locked state and continues to perform low resolution RF detection to detect motion.

[0107] If motion is detected at block 710, process 700 may proceed to block 714, where the device may execute a high-resolution RF detection algorithm (e.g., based on head orientation) to determine whether the user is facing the device. The high-resolution RF detection algorithm may differ from the medium-resolution RF detection algorithm by having a larger bandwidth, more spatial links, a higher sampling rate, or any combination thereof. In one illustrative example, the device may detect head orientation by configuring the RF interface to transmit a signal with a bandwidth of 80-160 MHz utilizing three or more spatial links and utilizing a sampling rate that is less than 50 ms.

[0108] At block 716, the device may determine whether the user is facing the device based on the RF detection data. If the user is not facing the device, process 700 may continue to perform a high-resolution RF detection algorithm at block 718. In some examples, the high-resolution RF detection algorithm may be used to detect motion at block 706. If no motion is detected, process 700 may return to block 704, and the device remains locked and continues to perform low-resolution RF detection to detect motion. If at block 716, the device determines that the user's head is facing the device, process 700 may proceed to block 720 and begin facial authentication.

[0109] FIG. 8 is a flowchart illustrating an example of a general authentication process 800 using face as biometric data. An input image 802 of a user attempting to access a device is acquired. For example, the input image 802 can be an image compiled using RF sensing techniques. In one example, the device may utilize an RF interface capable of transmitting extremely high frequency (EHF) signals or mm-wave technology (e.g., IEEE 802.11ad) to transmit signals in a direction perpendicular to the device screen. For example, the device may utilize the mm-wave RF interface to perform narrow beam sweeps to obtain time-of-flight and phase measurements at different angles from signals reflected from the user's face. In some examples, the device may utilize the time-of-flight and phase measurements to generate a facial signature that the device can compare to calibrated facial metrics stored in the system for facial recognition. In another example, the input image 802 can be acquired by a camera (e.g., input device 172) of the wireless device. In another example, the input image 802 can be acquired by using a LIDAR sensor (e.g., communication interface 1240) of the wireless device.

[0110] At block 804, the input image 802 is processed for feature extraction. For example, at block 804, a feature representation including one or more facial features may be extracted from the input image 802 including a face. The facial feature representation may be compared to facial representations of persons authorized to access the device (e.g., stored as templates in template storage 808). In some examples, the template storage 808 may include a database. In some examples, the template storage 808 is part of the same device that performs facial authentication (e.g., the user device 107, the wireless device 200, or another device). In some examples, the template storage 808 may be located remotely from the device that performs facial authentication (e.g., the wireless device 200) (e.g., performing facial authentication on a remote server in communication with the device).

[0111] The templates in template storage 808 can be generated during an enrollment step, which enrolls biometric features to be later used during authentication. Each template can be linked internally (e.g., in template storage 808) with a subject identifier (ID) unique to the person being enrolled. For example, during enrollment (sometimes referred to as registration), the owner of the computing device and / or other users with access to the computing device can input one or more biometric data samples (e.g., an image, a fingerprint sample, a voice sample, or other biometric data). Representative features of the biometric data can be extracted by a feature extraction engine. The representative features of the biometric data can be stored as one or more templates in template storage 808. For example, several images of the owner or user can be captured, each with a different pose, position, facial expression, lighting condition, and / or other characteristics.

[0112] In another example, several different facial signatures can be captured using RF sensing techniques. The facial features of each different image or signature can be extracted and saved as a template. For example, a template can be stored for each image / signature, with each template representing each facial feature with a unique pose, position, facial expression, lighting condition, etc. One or more templates stored in template storage 808 can be used as reference points for performing facial authentication.

[0113] As described above, in block 804, one or more facial features may be extracted from the input image 802. Any suitable feature extraction technique may be used to extract features from biometric data (during enrollment and authentication). An illustrative example of a feature extraction process that may generate deep learning features is neural network (e.g., using a deep learning network)-based feature extraction. For example, a neural network may be trained using multiple training images to learn the distinctive features of various faces. In one configuration, the neural network may be trained using RF sensed data corresponding to features associated with RF face signatures. After training, the trained neural network may be applied to an input image 802 containing a face. The trained neural network may extract or determine the distinctive features of the face. The neural network may be a classification network that includes a hidden convolutional layer that applies kernels (also called filters) to the input image to extract features.

[0114] At block 806, a similarity may be calculated between the user feature representation extracted from the input image 802 and the human facial feature representation stored in template storage 808. For example, the feature representation extracted from the input image 802 may be compared to one or more templates stored in template storage 808. For example, at block 806, process 800 may perform a similarity calculation to calculate a similarity between the input image 802 and one or more templates in template storage 808. The calculated similarity may be used as a similarity score 807 that is used to make a final authentication decision.

[0115] In some cases, the data of the input image 802 may be referred to as query data (e.g., a query face). In some cases, the template may be referred to as enrollment data (e.g., an enrollment face). As described above, in some examples, features extracted for a face (or other object or biometric feature) may be represented using a feature vector representing the face (or other object or biometric feature). For example, each template may be a feature vector. A representation of features extracted from the input biometric data may also be a feature vector. Each feature vector may include several values ​​representing the extracted features. The values ​​of the feature vector may include any suitable values. In some cases, the values ​​of the feature vector may be floating-point numbers between −1 and 1, i.e., normalized feature vector values. A feature vector representing facial features from the input image 802 may be compared or matched with one or more feature vectors of one or more templates to determine a similarity between the feature vectors. For example, a similarity between the feature vector representing the face in the input image 802 and the feature vector of each template may be determined to obtain multiple similarity values.

[0116] In some implementations, the similarity between the features of the enrollment face of the template (from template storage 808) and the features of the query face (in the input image 802) can be measured using distance. Any suitable distance can be used, including cosine distance, Euclidean distance, Manhattan distance, Mahalanobis distance, absolute difference, Hadamard product, polynomial mapping, element-wise multiplication, and / or other suitable distance. In one illustrative example, the similarity between two faces can be calculated as the sum between the similarities of two face patches. In some cases, the sum of similarities can be based on the sum of absolute differences (SAD) between the query face (in the input image 802) and the enrollment face of the template (from template storage 808).

[0117] One way to represent similarity is to use a similarity score (also called a match score). The similarity score represents the similarity between features (indicating how well the features match), with a higher score between two feature vectors indicating a higher similarity than a lower score between the two feature vectors. Referring to FIG. 8, a similarity score 807 indicates the similarity between one or more features of the stored templates and the facial features extracted from the input image 802. The device can compare the similarity score 807 with one or more thresholds. In some cases, a similarity score between the query face (in the input image 802) and each enrolled face (corresponding to each template) can be determined. The highest similarity score (corresponding to the highest match) can be used as the similarity score 807.

[0118] In some examples, a similarity score may be generated based on a calculated distance between facial features extracted from the input image 802 and the template data, or based on any other comparison metric. As previously described, distances may include cosine distance, Euclidean distance, Manhattan distance, Mahalanobis distance, absolute difference, Hadamard product, polynomial mapping, element-wise multiplication, and / or other suitable distances. As previously described, feature vectors for faces may be generated based on feature extraction performed by a feature extraction engine. A similarity score between a face in the input image 802 and the template data may be calculated based on the distance between the feature vector representing the face and the feature vector representing the template data. The calculated distance represents the difference between the data values ​​of the feature vector representing the face in the input image 802 and the feature vector representing the template data. For example, cosine distance measures the cosine of the angle between two non-zero vectors in an inner product space. Cosine similarity represents a measure of similarity between two non-zero vectors.

[0119] In some cases, the calculated distance (e.g., cosine distance, Euclidean distance, and / or other distance) may be normalized to a value of 0 or a value of 1. As an example, a similarity score may be defined as 1000*(1-distance). In some cases, the similarity score may be a value between 0 and 1.

[0120] As described above, the similarity score 807 can be used to make a final authentication decision. For example, in block 810, the similarity score 807 can be compared to a similarity threshold. In some examples, the similarity threshold can include a percentage of similarity (e.g., 75%, 80%, 85%, etc. of features are similar). If the similarity score 807 is greater than the similarity threshold, the device is unlocked in block 812. However, if the similarity score 807 is not greater than the threshold, the device remains locked in block 814.

[0121] In some aspects, authentication process 800 may be implemented with more than one similarity threshold. For example, the device may unlock at block 812 if the similarity score 807 is greater than a “high” threshold. In another example, the device may remain locked if the similarity score 807 is less than a “low” threshold. In some cases, if the similarity score 807 is less than the “low” threshold, the user may be presented with an alternative unlock mechanism (e.g., a fingerprint scan, an access code, etc.). In some examples, a similarity score 807 that is less than the “high” threshold (e.g., between the “high” and “low” thresholds) may cause the device to prompt the user to perform a new face scan (e.g., using RF detection techniques described herein).

[0122] In some implementations, devices that utilize facial authentication (e.g., mobile devices such as phones) implement an unlock timeout period. The unlock timeout period is a period of inactivity on the device (which is unlocked), after which the device automatically locks and a new facial authentication must be performed to unlock the device. In some examples, such devices may also implement a separate screen timeout period. The screen timeout period is a period of inactivity on the device (the device's screen or display is active or "on"), after which the device's screen or display is automatically turned off (e.g., the screen or display is powered off). The device may continue to remain unlocked when the screen or display is turned off.

[0123] 9 is a flow diagram illustrating an example process 900 for performing device management based on user attention. Block 902 illustrates a device state in which the device is unlocked and the device's screen (or display) is on. For example, a user may have provided authentication information (e.g., facial recognition, fingerprint recognition, access code, etc.) to access the device and then begin using the device.

[0124] At block 904, the device may perform RF detection to determine whether the user's attention is still directed toward the device. For example, the device may be configured to perform one or more operations related to RF detection described in connection with process 500 to determine the presence of a user (e.g., head detection) and / or one or more operations related to RF detection described in connection with process 600 to determine the orientation of the user's head. In some cases, the device may be configured to perform one or more operations related to RF detection described in connection with process 400 to determine movement (e.g., before performing the operations of process 500).

[0125] In block 906, the device may use the RF sensing data to determine whether the user's head is detected. If the head is not detected, the device may determine that the user is no longer using the device and may proceed to block 908. In block 908, the device is locked. In some configurations, the device may implement a timer before proceeding to block 908. For example, if the user's head is not detected for a certain amount of time (e.g., two minutes), the device may determine that the user is no longer present and may lock access to the device.

[0126] If, at block 906, the device determines that a user is present (e.g., a head is detected), the process may proceed to block 910 to determine the user's head orientation. For example, the user may be holding the device but having a conversation with another person such that the user's face is not facing the device. In this example, the process may proceed to block 912 and may dim the display backlight to conserve power and / or battery life. In some configurations, the device may proceed to block 912 after a period of inattention has passed (e.g., the user's head has been misoriented for two minutes).

[0127] If, at block 910, the device determines that the user is facing the device, the process may proceed to block 914 and adjust one or more settings on the device. For example, the device may detect that the display backlight was previously dimmed (at block 912), in which case the backlight should be brightened because the user's attention is now directed toward the device. In another example, the device may adjust the volume of an alert (e.g., decrease the alert volume) in response to determining that the user's attention is directed toward the device. For example, the device may decrease the device ring volume in response to determining that the user is looking at the device screen. After adjusting the device setting, the process may return to block 904 and continue to perform RF sensing to determine the user's attention.

[0128] 10 is a flow chart illustrating an example of a process 1000 for performing facial recognition. At operation 1002, the process 1000 includes receiving, by a first wireless device, a first received waveform that is a reflection of a first RF waveform. In some examples, the first RF waveform is transmitted by the same device (by the first wireless device) as the device receiving the received waveform (e.g., a monostatic configuration). In other examples, a bistatic configuration may be implemented in which the first RF waveform may be transmitted by another wireless device (e.g., a second wireless device), such as an access point or any other type of wireless device having an RF interface. In some examples, the first RF waveform may include an omnidirectional antenna of the first wireless device or a Wi-Fi signal transmitted by an omnidirectional antenna on the first wireless device.

[0129] At operation 1004, process 1000 includes determining a user presence based on RF detection data associated with the first received waveform. In some examples, the RF detection data may include CSI data corresponding to reflections received in response to transmission of the first RF waveform. In other examples, the RF detection data may include data associated with at least one received leakage signal corresponding to the first RF waveform rather than a signal reflected from an object. The RF detection data may be used to detect a user presence, where detecting a user presence may include detecting user movement, detecting a user presence (e.g., head presence), detecting a user head orientation, or any combination thereof. In some examples, detecting a user presence may include tracking a user's movement and determining that the user is within a threshold distance of the wireless device.

[0130] In some aspects, the presence of a user can be detected by using RF sensing data to determine the distance and angle of arrival of a reflected signal. In some examples, the distance determination can be based on the time of flight of the reflected signal adjusted based on the direct path propagation delay (e.g., leakage signal between the transmit antenna and the receive antenna). In some examples, the angle of arrival can be based on the difference in signal phase measured at each element in the receive antenna array.

[0131] In some aspects, the first wireless device may transmit a second RF waveform having a larger bandwidth than the first RF waveform. The first wireless device may receive a second received waveform from the user that is a reflection of the second RF waveform, and determine at least one of the presence of the user's head or the orientation of the user's head based on RF sensing data associated with the second received waveform. In some examples, the second RF waveform may include a different number of spatial links than the first RF waveform.

[0132] In some examples, the presence of the user's head or the orientation of the user's head can be used to initiate further activity on the first wireless device and / or other wireless devices. For example, determining that the user's head is not present for a predetermined time (e.g., head presence is detected as false) can lock access to the wireless device. In another example, determining that the user's head is misoriented for a predetermined time can dim the display backlight on the first wireless device.

[0133] At operation 1006, process 1000 includes initiating facial authentication of the user in response to determining the presence of the user. As described above, the presence of the user may correspond to user movement, user presence (e.g., head detection), user head orientation, or any combination thereof. In some examples, facial authentication may be performed by configuring an RF interface on the first wireless device to transmit one or more extremely high frequency (EHF) waveforms (e.g., multiple EHF waveforms). The first wireless device can receive multiple reflected waveforms corresponding to the one or more EHF waveforms. RF sensing data corresponding to the received reflections corresponding to the EHF waveforms can be used to generate a facial signature (associated with the user) that can be used to perform facial authentication. In some cases, initiating facial authentication of the user can include capturing an image of the user's face using an infrared camera.

[0134] In some aspects, the first wireless device may determine, based on facial authentication, that the user is authorized to access the first wireless device. For example, the first wireless device may calculate a similarity score (e.g., similarity score 807) and determine that the similarity score meets or exceeds a similarity threshold for enabling access to the first wireless device. In some examples, the first wireless device may enable access to the first wireless device in response to determining (based on facial authentication) that the user is authorized to access the first wireless device.

[0135] In some cases, the first wireless device may determine, based on facial authentication, that the user is authorized to access one or more other wireless devices. In some aspects, the first wireless device may enable access to at least one wireless device in the one or more wireless devices in response to determining that the user is authorized to access the one or more other wireless devices. In one illustrative example, the first wireless device may correspond to a mobile device (e.g., a smartphone or tablet), and at least one wireless device in the one or more wireless devices may correspond to a vehicle, which can be unlocked, started, enabled, or otherwise accessed based on user authentication with the first wireless device. In another illustrative example, the first wireless device may correspond to a mobile device (e.g., a smartphone or tablet), and at least one wireless device in the one or more wireless devices may correspond to an Internet of Things (IoT) device that can be configured to enable access to a home (e.g., unlock a door or open a garage door).

[0136] 11 is a flow chart illustrating an example of a process 1100 for performing head detection. At operation 1102, the process 1100 includes receiving, by a wireless device, a first received waveform that is a reflection of a first RF waveform. In some examples, the first RF waveform is transmitted by the same device (by the wireless device) as the device that receives the first received waveform (e.g., a monostatic configuration). In other examples, a bistatic configuration may be implemented in which the first RF waveform may be transmitted by another wireless device, such as an access point or any other type of wireless device with an RF interface. In some examples, the first RF waveform may include an omnidirectional antenna of the first wireless device or a Wi-Fi signal transmitted by an omnidirectional antenna on the first wireless device.

[0137] At operation 1104, process 1100 includes determining at least one of a presence of a user's head or an orientation of the user's head based on RF detection data associated with the first received waveform. In some examples, the RF detection data may include CSI data corresponding to reflections received in response to transmission of the first RF waveform. In other examples, the RF detection data may include data associated with at least one received leakage signal corresponding to the first RF waveform rather than a signal reflected from an object. In some aspects, determining the presence of the user's head may include determining that the user's head is within a threshold distance of the wireless device. In some examples, determining the orientation of the user's head may include determining that the user is facing toward the wireless device for a predetermined time period.

[0138] In some examples, the wireless device may implement one or more different RF detection algorithms to determine or detect the presence and / or orientation of a user's head. In some cases, the RF detection algorithms may have various levels of resolution and / or power consumption. In some instances, the resolution and / or power consumption of the RF detection algorithms may be based on a bandwidth, a number of spatial links, a sampling rate, or any combination thereof. In some aspects, the wireless device may initiate facial recognition if it determines that the user's head is oriented toward the device. In a further example, the wireless device may determine that the user's head is oriented toward a different device. The wireless device may use device location data, device orientation data, indoor map data, or any other suitable data thereof to identify other devices located in the user's vicinity. In some cases, the wireless device may communicate with other devices based on a determination of the user's presence and / or the user's head orientation. For example, if the wireless device determines that the user is oriented toward and / or near a television, it may transmit a signal to enable the television to be turned on.

[0139] In some examples, the processes described herein (e.g., processes 400, 500, 600, 700, 800, 900, 1000, and / or other processes described herein) may be performed by a computing device or apparatus (e.g., a UE). In one example, process 1000 may be performed by user device 107 of FIG. 1. In another example, process 1000 may be performed by a computing device with computing system 1200 shown in FIG. 12. For example, a computing device with the computing architecture shown in FIG. 12 may include the components of user device 107 of FIG. 1 and may perform the operations of FIG. 10.

[0140] In some cases, a computing device or apparatus may include various components, such as one or more input devices, one or more output devices, one or more processors, one or more microprocessors, one or more microcomputers, one or more cameras, one or more sensors, and / or other components configured to perform the steps of processes described herein. In some examples, a computing device may include a display, one or more network interfaces configured to communicate and / or receive data, any combination thereof, and / or other components. The one or more network interfaces may be configured to communicate and / or receive wired and / or wireless data, including data according to 3G, 4G, 5G, and / or other cellular standards, data according to the WiFi (802.11x) standard, data according to the Bluetooth™ standard, data according to the Internet Protocol (IP) standard, and / or other types of data.

[0141] Components of a computing device may be implemented with circuitry. For example, a component may include and / or be implemented using electronic circuitry or other electronic hardware, which may include one or more programmable electronic circuits (e.g., a microprocessor, a graphics processing unit (GPU), a digital signal processor (DSP), a central processing unit (CPU), and / or other suitable electronic circuitry), and / or may include and / or be implemented using computer software, firmware, or any combination thereof, to perform various operations described herein.

[0142] Process 1000 is illustrated as a logical flow diagram, whose operations represent a sequence of actions that can be implemented in hardware, computer instructions, or a combination thereof. In the context of computer instructions, the actions represent computer-executable instructions stored on one or more computer-readable storage media that, when executed by one or more processors, perform the described actions. Generally, computer-executable instructions include routines, programs, objects, components, data structures, etc. that perform particular functions or implement particular data types. The order in which the actions are described is not intended to be construed as a limitation, and any number of the described actions may be combined in any order and / or in parallel to implement the process.

[0143] Additionally, process 1000, and / or other processes described herein, may be executed under the control of one or more computer systems configured with executable instructions and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) that collectively execute on one or more processors, by hardware, or a combination thereof. As noted above, the code may be stored on a computer-readable or machine-readable storage medium, for example, in the form of a computer program comprising instructions executable by one or more processors. The computer-readable or machine-readable storage medium may be non-transitory.

[0144] 12 is a diagram illustrating an example of a system for implementing some aspects of the present technology. Specifically, FIG. 12 illustrates an example of a computing system 1200, which may be, for example, an internal computing system, a remote computing system, a camera, or any computing device comprising any of these components, with the components of the system communicating with each other using connections 1205. The connections 1205 may be physical connections using a bus or direct connections to a processor 1210, such as in a chipset architecture. The connections 1205 may also be virtual, network, or logical connections.

[0145] In some embodiments, computing system 1200 is a distributed system in which the functionality described in this disclosure may be distributed across a data center, multiple data centers, a peer network, etc. In some embodiments, one or more of the described system components represent many components, each performing some or all of the functionality for which the component is described. In some embodiments, the components may be physical or virtual devices.

[0146] Exemplary system 1200 includes at least one processing unit (CPU or processor) 1210 and connections 1205 that communicatively couple various system components to the processor 1210, including system memory 1215, such as read-only memory (ROM) 1220 and random access memory (RAM) 1225. The computing system 1200 may include a cache 1212 of high-speed memory directly connected to, near, or integrated as part of the processor 1210.

[0147] Processor 1210 may include any general-purpose processor and hardware or software services, such as services 1232, 1234, and 1236 stored on storage device 1230, configured to control processor 1210 as well as special-purpose processors where software instructions are incorporated into the actual processor design. Processor 1210 may essentially be a completely self-contained computing system, including multiple cores or processors, buses, memory controllers, caches, etc. Multi-core processors may be symmetric or asymmetric.

[0148] To enable user interaction, computing system 1200 includes input devices 1245, which may represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, a keyboard, a mouse, motion input, speech, etc. Computing system 1200 may also include output devices 1235, which may be one or more of a number of output mechanisms. In some instances, a multimodal system may enable a user to provide multiple types of input / output to communicate with computing system 1200.

[0149] The computing system 1200 may include a communications interface 1240, which may generally govern and manage user input and system output. The communications interface may be an audio jack / plug, a microphone jack / plug, a Universal Serial Bus (USB) port / plug, an Apple® Lightning® port / plug, an Ethernet port / plug, an optical fiber port / plug, a proprietary wired port / plug, 3G, 4G, 5G, and / or other cellular data network wireless signaling, Bluetooth® wireless signaling, Bluetooth® low energy (BLE) wireless signaling, IBEACON® wireless signaling, radio frequency identification (RFID) wireless signaling, near field communication (NFC) wireless signaling, dedicated short range communication (DSRC) wireless signaling, 802.11 Wi-Fi wireless signaling, a wireless local area network (WLAN) signaling, visible light communication (VLC), Worldwide Interoperability for Microwave The device may perform or facilitate the reception and / or transmission of wired or wireless communications using wired and / or wireless transceivers, including those utilizing WiMAX (Wireless Access), infrared (IR) communications wireless signal transmission, public switched telephone network (PSTN) signal transmission, integrated services digital network (ISDN) signal transmission, ad hoc network signal transmission, radio wave signal transmission, microwave signal transmission, infrared signal transmission, visible light signal transmission, ultraviolet light signal transmission, wireless signal transmission along the electromagnetic spectrum, or any combination thereof.

[0150] Communications interface 1240 may also include one or more range sensors (e.g., light detection and ranging (LIDAR) sensors, laser range finders, radar, ultrasonic sensors, infrared (IR) sensors) configured to collect data and provide measurements to processor 1210, which can be configured to perform the determinations and calculations necessary to obtain various measurements for the one or more range sensors. In some examples, the measurements may include time of flight, wavelength, azimuth angle, elevation angle, range, linear velocity, and / or angular velocity, or any combination thereof. Communications interface 1240 may also include one or more GNSS receivers or transceivers used to determine the position of computing system 1200 based on reception of one or more signals from one or more satellites associated with one or more Global Navigation Satellite System (GNSS) systems. GNSS systems include, but are not limited to, the United States' Global Positioning System (GPS), the Russian Global Navigation Satellite System (GLONASS), the Chinese BeiDou Navigation Satellite System (BDS), and the European Galileo GNSS. Since there is no restriction to operating on any particular hardware configuration, the basic features herein may be easily replaced by improved hardware or firmware configurations as they are developed.

[0151] The storage device 1230 can be a non-volatile and / or non-transitory and / or computer-readable memory device, such as a hard disk, or a magnetic cassette, a flash memory card, a solid-state memory device, a digital versatile disk, a cartridge, a floppy disk, a flexible disk, a hard disk, a magnetic tape, a magnetic strip / stripe, any other magnetic storage medium, a flash memory, a memristor memory, any other solid-state memory, a compact disk read-only memory (CD-ROM), an optical disk, a rewritable compact disk (CD) optical disk, a digital video disk (DVD) optical disk, a Blu-ray Disc (BDD) optical disk, a holographic optical disk, other optical media, a secure digital (SD) card, a micro secure digital (microSD) card, a memory It may be other types of computer-readable media capable of storing data accessible by a computer, such as a Stick® card, smart card chip, EMV chip, subscriber identity module (SIM) card, mini / micro / nano / pico SIM card, other integrated circuit (IC) chip / card, random access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash EPROM (FLASHEPROM), cache memory (e.g., level 1 (L1) cache, level 2 (L2) cache, level 3 (L3) cache, level 4 (L4) cache, level 5 (L5) cache, or other (L#) cache), resistive random access memory (RRAM / ReRAM), phase change memory (PCM), spin-transfer torque RAM (STT-RAM), other memory chips or cartridges, and / or any combination thereof.

[0152] Storage devices 1230 may include software services, servers, services, etc., that cause the system to perform functions when code defining such software is executed by processor 1210. In some embodiments, hardware services that perform specific functions may include software components stored on computer-readable media that interface with necessary hardware components, such as processor 1210, connections 1205, output devices 1235, etc., to perform the functions. The term "computer-readable medium" includes, but is not limited to, portable or non-portable storage devices, optical storage devices, and various other media that can store, contain, or carry instructions and / or data. Computer-readable media may also include non-transitory media on which data may be stored and that do not include carrier waves and / or transitory electronic signals propagating wirelessly or via wired connections. Examples of non-transitory media may include, but are not limited to, magnetic disks or tapes, optical storage media such as compact discs (CDs) or digital versatile discs (DVDs), flash memory, memory, or memory devices. A computer-readable medium may store code and / or machine-executable instructions, which may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and / or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc.

[0153] Although specific details have been provided in the above description to provide a thorough understanding of the embodiments and examples provided herein, those skilled in the art will recognize that the present application is not limited thereto. Accordingly, while exemplary embodiments of the present application have been described in detail herein, it should be understood that the concepts of the present invention may be embodied and employed in various other ways, and that the appended claims are intended to be construed to include such variations except insofar as limited by the prior art. Various features and aspects of the applications described above may be used individually or together. Furthermore, the embodiments may be utilized in any number of environments and applications other than those described herein without departing from the broader spirit and scope of the present specification. Accordingly, the specification and drawings should be considered illustrative and not restrictive. For illustrative purposes, methods have been described in a particular order. It should be appreciated that in alternative embodiments, methods may be performed in an order different from that described.

[0154] For clarity of explanation, in some instances, the technology may be presented as including individual functional blocks comprising devices, device components, and method steps or routines embodied in software, or a combination of hardware and software. Additional components other than those shown in the figures and / or described herein may be used. For example, circuits, systems, networks, processes, or other components may be shown as components in block diagram form to avoid obscuring the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

[0155] Furthermore, those skilled in the art will appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the aspects disclosed herein may be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0156] Individual embodiments may be described above as a process or method that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. While a flowchart may describe operations as a sequential process, many of the operations may be performed in parallel or simultaneously. In addition, the order of operations may be rearranged. A process terminates when its operations are completed, but may have additional steps not included in the diagram. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination may correspond to the function returning to the calling function or the main function.

[0157] The processes and methods according to the examples described above can be implemented using computer-executable instructions stored or otherwise available from a computer-readable medium. Such instructions can include, for example, instructions and data that cause a general-purpose computer, special-purpose computer, or processing device to perform a particular function or group of functions, or otherwise configure a general-purpose computer, special-purpose computer, or processing device to perform a particular function or group of functions. Portions of the computer resources used can be accessible over a network. The computer-executable instructions can be, for example, binary, intermediate format instructions such as assembly language, firmware, or source code. Examples of computer-readable media that can be used to store instructions, information used, and / or information created during methods according to the described examples include magnetic or optical disks, flash memory, USB devices provided with non-volatile memory, network-attached storage devices, etc.

[0158] In some embodiments, computer-readable storage devices, media, and memories may include cables or wireless signals containing bitstreams, etc. However, when referred to, non-transitory computer-readable storage media specifically excludes media such as energy, carrier signals, electromagnetic waves, and signals in its own right.

[0159] Those skilled in the art will understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, the data, instructions, commands, information, signals, bits, symbols, and chips that may be referred to throughout the above description may, in some cases, be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof, depending in part on the specific application, in part on the desired design, the corresponding technology, etc.

[0160] The various illustrative logical blocks, modules, and circuits described in connection with the aspects disclosed herein may be implemented or performed using hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof, and may take any of a variety of form factors. When implemented in software, firmware, middleware, or microcode, the program code or code segments (e.g., a computer program product) to perform the necessary tasks may be stored on a computer-readable or machine-readable medium. A processor may perform the necessary tasks. Examples of form factors include laptops, smartphones, mobile phones, tablet devices or other small form factor personal computers, personal digital assistants, rack-mounted devices, standalone devices, etc. The functionality described herein may also be embodied in peripheral devices or add-in cards. Such functionality may also be implemented on circuit boards in different chips or processes executing in a single device, as further examples.

[0161] The instructions, media for carrying such instructions, computing resources for executing the instructions, and other structures for supporting such computing resources are exemplary means for providing the functionality described in this disclosure.

[0162] The techniques described herein may also be implemented in electronic hardware, computer software, firmware, or any combination thereof. Such techniques may be implemented in any of a variety of devices, such as a general-purpose computer, a wireless communication device handset, or an integrated circuit device having multiple uses, including applications in wireless communication device handsets and other devices. Any features described as modules or components may be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. If implemented in software, the techniques may be realized at least in part by a computer-readable data storage medium comprising program code including instructions that, when executed, perform one or more of the methods, algorithms, and / or operations described above. The computer-readable data storage medium may form part of a computer program product, which may include packaging materials. The computer-readable medium may comprise a memory or data storage medium, such as a random access memory (RAM), such as synchronous dynamic random access memory (SDRAM), a read-only memory (ROM), a nonvolatile random access memory (NVRAM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic or optical data storage medium, etc. The techniques may additionally or alternatively be realized at least in part by a computer-readable communications medium, such as a propagated signal or wave, that carries or communicates program code in the form of instructions or data structures and that can be accessed, read, and / or executed by a computer.

[0163] The program code may be executed by a processor, which may include one or more processors, such as one or more digital signal processors (DSPs), general-purpose microprocessors, application-specific integrated circuits (ASICs), field-programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Such a processor may be configured to perform any of the techniques described in this disclosure. A general-purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Accordingly, the term “processor,” as used herein, may refer to any of the above structures, any combination of the above structures, or any other structure or apparatus suitable for implementing the techniques described herein.

[0164] Those skilled in the art will understand that the less than ("<") and greater than (">") symbols or terms used herein can be replaced with the less than or equal to ("≦") and greater than or equal to ("≧") symbols, respectively, without departing from the scope of this description.

[0165] When a component is described as being "configured to" perform some operations, such configuration may be achieved, for example, by designing electronic circuitry or other hardware to perform the operations, by programming programmable electronic circuitry (e.g., a microprocessor or other suitable electronic circuitry) to perform the operations, or any combination thereof.

[0166] The phrases "coupled to" or "communicatively coupled to" refer to any component that is physically connected to another component, either directly or indirectly, and / or that is in communication, either directly or indirectly, with another component (e.g., connected to the other component via a wired or wireless connection, and / or other suitable communication interface).

[0167] Claim language or other language reciting "at least one of" a set and / or "one or more" of a set indicates that one element of the set or multiple elements of the set (in any combination) satisfies the claim. For example, claim language reciting "at least one of A and B" or "at least one of A or B" means A, B, or A and B. In another example, claim language reciting "at least one of A, B, and C" or "at least one of A, B, or C" means A, B, C, or A and B, or A and C, or B and C, or A and B and C. The language "at least one of" a set and / or "one or more" of a set does not limit the set to the items listed in the set. For example, claim language reciting "at least one of A and B" or "at least one of A or B" can mean A, B, or A and B, and can additionally include items not listed in the set of A and B.

[0168] Exemplary aspects of the present disclosure include the following.

[0169] Aspect 1: A first wireless device for facial recognition. The first wireless device includes at least one transceiver, at least one memory, and at least one processor coupled to the at least one transceiver and the at least one memory. The at least one processor is configured to receive, via the at least one transceiver, a first received waveform that is a reflection of a first radio frequency (RF) waveform, determine a presence of a user based on RF sensing data associated with the first received waveform, and initiate facial authentication of the user in response to determining the presence of the user.

[0170] Aspect 2: The first wireless device of Aspect 1, wherein at least one processor is configured to transmit a second RF waveform via at least one transceiver, the second RF waveform having a bandwidth greater than the first RF waveform, receive a second received waveform from a user via the at least one transceiver, the second received waveform being a reflection of the second RF waveform, and determine at least one of the presence of the user's head or the orientation of the user's head based on RF sensing data associated with the second received waveform.

[0171] Aspect 3: A first wireless device described in either aspect 1 or 2, wherein at least one processor is configured to dim a display backlight on the first wireless device in response to determining that the orientation of the user's head is misaligned for a predetermined period of time.

[0172] Aspect 4: The first wireless device described in any one of aspects 1 to 3, wherein at least one processor is configured to lock access to the first wireless device in response to determining that the presence of the user's head is false for a predetermined period of time.

[0173] Aspect 5: The first wireless device of any one of Aspects 1 to 4, wherein the first RF waveform includes a Wi-Fi signal transmitted by an omnidirectional antenna on the first wireless device.

[0174] Aspect 6: The first wireless device of any one of Aspects 1 to 5, wherein the first RF waveform is transmitted by a second wireless device.

[0175] Aspect 7: The first wireless device of any one of aspects 1 to 6, wherein the RF sensing data includes channel state information (CSI) data.

[0176] Aspect 8: The first wireless device described in any one of aspects 1 to 7, wherein at least one processor is configured to track the movement of a user to determine the presence of the user and to determine that the user is within a threshold distance to the first wireless device.

[0177] Aspect 9: A first wireless device described in any one of aspects 1 to 8, wherein at least one processor is configured to transmit a plurality of extremely high frequency (EHF) waveforms via at least one transceiver to initiate facial authentication of a user, receive a plurality of reflected waveforms corresponding to the plurality of EHF waveforms via the at least one transceiver, and generate a facial signature associated with the user based on RF sensing data associated with the plurality of reflected waveforms.

[0178] Aspect 10: The first wireless device of any one of aspects 1 to 9, further comprising a camera, wherein the at least one processor is configured to capture at least one image of the user's face using the camera to initiate facial authentication of the user.

[0179] Aspect 11: The first wireless device of any one of aspects 1 to 10, wherein the RF detection data includes data related to at least one received leakage signal corresponding to the first RF waveform, rather than a signal reflected from an object.

[0180] Aspect 12: The first wireless device described in any one of aspects 1 to 11, wherein at least one processor is configured to determine that a user is authorized to access the first wireless device based on facial authentication, and to enable access to the first wireless device in response to determining that the user is authorized to access the first wireless device.

[0181] Aspect 13: A first wireless device described in any one of aspects 1 to 12, wherein at least one processor is configured to determine, based on facial authentication, that the user is authorized to access the one or more other wireless devices, and in response to determining that the user is authorized to access the one or more other wireless devices, enable access to the at least one wireless device on the one or more other wireless devices.

[0182] Aspect 14: A method of performing facial recognition, the method comprising the operations of any one of aspects 1 to 13.

[0183] Aspect 15: A computer-readable medium comprising at least one instruction for causing a computer or processor to perform an operation according to any one of aspects 1 to 13.

[0184] Aspect 16: An apparatus for facial recognition, comprising means for performing the operations according to any one of aspects 1 to 13.

[0185] Aspect 17: A wireless device for determining a user's presence. The wireless device includes at least one transceiver, at least one memory, and at least one processor coupled to the at least one transceiver and the at least one memory. The at least one processor is configured to: process, by the wireless device, a first received waveform that is a reflection of a first radio frequency (RF) waveform; determine a user's presence based on RF detection data associated with the first received waveform; transmit, via the at least one transceiver, a second RF waveform having a higher bandwidth than the first RF waveform in response to determining the user's presence; process the second received waveform that is a reflection of the second RF waveform from the user; and determine at least one of a user's head presence or an orientation of the user's head based on the RF detection data associated with the second received waveform.

[0186] Aspect 18: The wireless device of aspect 17, wherein at least one processor is configured to dim a display backlight on the first wireless device in response to determining that the orientation of the user's head is misaligned for a predetermined period of time.

[0187] Aspect 19: The wireless device of aspect 17 or 18, wherein at least one processor is configured to lock access to the first wireless device in response to determining that the presence of the user's head is false for a predetermined period of time.

[0188] Aspect 20: The wireless device of any one of aspects 17 to 19, wherein the first RF waveform includes a Wi-Fi signal transmitted by an omnidirectional antenna on the first wireless device.

[0189] Aspect 21: The wireless device of any one of aspects 17 to 20, wherein the RF sensing data includes channel state information (CSI) data.

[0190] Aspect 22: A first wireless device described in any one of aspects 17 to 21, wherein at least one processor is configured to track the movement of a user to determine the presence of the user and to determine that the user is within a threshold distance to the first wireless device.

[0191] Aspect 23: The wireless device of any one of aspects 17 to 22, wherein the RF sensing data includes data related to at least one received leakage signal corresponding to the first RF waveform, rather than a signal reflected from the object.

[0192] Aspect 24: A method for determining the presence of a user, the method comprising the operations of any one of aspects 17 to 23.

[0193] Aspect 25: A computer-readable medium comprising at least one instruction for causing a computer or processor to perform an operation according to any one of aspects 17 to 23.

[0194] Aspect 26: An apparatus for determining a presence of a user, the apparatus comprising means for performing the operations according to any one of aspects 17 to 23. [Explanation of symbols]

[0195] 107 User Devices 170 Computing Systems 172 Input Devices 174 Subscriber Identity Module (SIM) 176 Modem 178 Wireless Transceiver 180 output devices 182 Digital Signal Processor (DSP) 184 processors 186 Memory Devices 187 Antenna 188 Signal 189 Bus 200 Wireless Devices 202 users 204 Digital-to-Analog Converter (DAC) 206 RF Transmitter 208 Analog-to-Digital Converter (ADC) 210 RF Receiver 212 TX antenna 214 RX antenna 216 TX waveform 218 RX waveform 300 Environment 302 Wireless Devices 304 Access Point (AP) 306 Antenna 308 users 309a First user position 309b Second User Location 309c position 310a signal 310b signal 312 Reflected signal 314 RF signal 316 Reflected signal 318 RF signal 320 reflected signal 400, 500, 600, 700, 800, 900, 1000, 1100 processes 802 input images 807 Similarity Score 808 Template Storage 1002, 1004, 1006, 1102, 1104 operation 1200 Computing System 1205 Connection 1210 processor 1212 Cache 1215 system memory 1220 Read-Only Memory (ROM) 1230 Storage Devices 1232, 1234, 1236 services 1235 output device 1240 communication interface

Claims

1. 1. A first wireless device for facial recognition, comprising: at least one transceiver; At least one memory; at least one processor coupled to the at least one memory and the at least one transceiver, receiving, via the at least one transceiver, a first received waveform that is a reflection of a first radio frequency (RF) waveform; determining a user presence based on RF sensing data associated with the first received waveform; at least one processor configured to Equipped with To determine the presence of the user, the at least one processor: tracking the user's movements; determining that the user is within a threshold distance to the first wireless device; configured to: In response to determining the presence of the user, the at least one processor: transmitting, via the at least one transceiver, a second RF waveform having a larger bandwidth than the first RF waveform; receiving a second received waveform from the user via the at least one transceiver, the second received waveform being a reflection of the second RF waveform; determining at least one of a presence of the user's head or an orientation of the user's head based on RF sensed data associated with the second received waveform; initiating face authentication of the user in response to determining at least one of the presence of the head of the user or the orientation of the head of the user; configured to: The first wireless device.

2. the at least one processor:

10. The first wireless device of claim 1, configured to dim a display backlight on the first wireless device in response to determining that the user's head orientation is misaligned for a predetermined period of time.

3. The at least one processor:

10. The first wireless device of claim 1, configured to lock access to the first wireless device in response to determining that the presence of the user's head is false for a predetermined period of time.

4. 10. The first wireless device of claim 1, wherein the first RF waveform comprises a Wi-Fi signal transmitted by an omni-directional antenna on the first wireless device.

5. 10. The first wireless device of claim 1, wherein the first RF waveform is transmitted by a second wireless device.

6. 10. The first wireless device of claim 1, wherein the RF sensing data includes channel state information (CSI) data.

7. the at least one processor, to initiate facial authentication of the user, transmitting a plurality of extremely high frequency (EHF) waveforms via the at least one transceiver; receiving, via the at least one transceiver, a plurality of reflected waveforms corresponding to the plurality of EHF waveforms; generating a facial signature associated with the user based on RF sensing data associated with the plurality of reflected waveforms; The first wireless device of claim 1 , configured to:

8. 1. A method of performing facial recognition, comprising: receiving, by a first wireless device, a first received waveform that is a reflection of the first radio frequency (RF) waveform; determining the presence of a user based on RF sensing data associated with the first received waveform; Including, determining the presence of the user, tracking the user's movements; determining that the user is within a threshold distance to the first wireless device; Including, The step of determining the presence of the user includes, in response to determining the presence of the user, transmitting, by the first wireless device, a second RF waveform having a larger bandwidth than the first RF waveform; receiving, by the first wireless device, a second received waveform from the user that is a reflection of the second RF waveform; determining at least one of the presence of the user's head or the orientation of the user's head based on RF sensed data associated with the second received waveform; initiating facial authentication of the user in response to determining at least one of the presence of the head of the user or the orientation of the head of the user; A method comprising:

9. a display of the first wireless device is on, and the method further comprises: determining that a predetermined period of time has elapsed since detecting the presence of the user's head; turning off the display of the first wireless device based on a determination that the predetermined period of time has elapsed; 9. The method of claim 8, comprising:

10. 10. The method of claim 8, further comprising the step of locking access to the first wireless device in response to determining that the head presence of the user is false for a predetermined time period.

11. 10. The method of claim 8, wherein the first RF waveform comprises a Wi-Fi signal transmitted by an omni-directional antenna on the first wireless device.

12. The method of claim 8 , wherein the first RF waveform is transmitted by a second wireless device.

13. The method of claim 8 , wherein the RF sensing data includes channel state information (CSI) data.

14. The step of initiating face authentication of the user includes: transmitting a plurality of extremely high frequency (EHF) waveforms; receiving a plurality of reflected waveforms corresponding to the plurality of EHF waveforms; generating a facial signature associated with the user based on RF sensing data associated with the plurality of reflected waveforms; 9. The method of claim 8, comprising:

15. A computer readable medium comprising at least one instruction, said at least one instruction causing a computer or processor to perform the method of any one of claims 8 to 14.

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