Facial recognition using radio frequency sensing

Abstract

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

Description

[Technical Field]

[0001] This disclosure generally relates to detecting the presence of a user and / or performing facial recognition. Aspects of this disclosure relate to systems and techniques for using radio frequency (RF) sensing to detect the presence of a user and / or perform facial recognition. [Previous Technology]

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

[0003] Wireless electronic devices may also include hardware and software components that can be used to unlock the device based on biometrics associated with an authorized user, such as facial or fingerprint authentication. To enable various telecommunications functions, wireless electronic devices may include hardware and software components configured to transmit and receive radio frequency (RF) signals. For example, wireless devices may be configured to communicate via Wi-Fi, 5G / New Radio (NR), Bluetooth™, and / or Ultra Wideband (UWB), etc. [Summary of the Invention]

[0004] The following is a simplified summary relating to one or more aspects disclosed herein. Therefore, this summary should not be considered a broad overview relating to all anticipated aspects, nor should it be considered an identification of key or important elements relating to all anticipated aspects, or a description of the scope relating to any particular aspect. Thus, the sole purpose of this summary is to present, in a simplified form, certain concepts relating to one or more aspects of the mechanisms disclosed herein before the detailed description given below.

[0005] Systems, methods, apparatuses, and computer-readable media for performing facial recognition are disclosed. According to at least one example, a method for performing facial recognition is provided. The method may include: receiving a first received waveform as a reflection of a first radio frequency (RF) waveform via a first wireless device; determining the presence of a user based on RF sensing data associated with the first received waveform; and in response to determining the presence of the user, initiating facial authentication of the user.

[0006] In another example, a wireless device for facial recognition is provided, comprising at least one transceiver, at least one storage memory, and at least one processor (e.g., in a circuit configuration) coupled to the at least one storage memory and the at least one transceiver. The at least one processor is configured to: receive a first received waveform as a reflection of a first radio frequency (RF) waveform via the at least one transceiver; determine the 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 is provided, the medium including at least one instruction stored thereon, which, when executed by one or more processors, causes one or more processors to: receive a first received waveform as a reflection of a first radio frequency (RF) waveform via a first wireless device; determine the presence of a user based on RF sensing data associated with the first received waveform; and, in response to determining the presence of the user, initiate facial authentication of the user.

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

[0009] In another example, a method for determining the presence of a user is provided. The method may include: processing a first received waveform as a reflection of a first radio frequency (RF) waveform via a wireless device; determining the presence of a user 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 presence of a user; processing the second received waveform as a reflection of the second RF waveform from the user; and determining at least one of the presence of a user's head or the direction of the user's head based on RF sensing data associated with the second received waveform.

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

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

[0013] In some aspects, the device is a wireless device or 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 computer, a personal computer, a laptop computer, a server computer, a wireless access point, a vehicle or vehicle component, or any other device having an RF interface.

[0014] Other objects and advantages associated with the aspects disclosed herein will be apparent to those skilled in the art from the accompanying drawings and detailed description.

Implementation Method

[0028] Certain aspects and embodiments of this disclosure are provided below for illustration. Alternative aspects may be designed without departing from the scope of this disclosure. Furthermore, known elements of this disclosure will not be described in detail or will be omitted to avoid obscuring relevant details of this disclosure. Some aspects and embodiments described herein can be applied independently, and some of them can be combined, as will be apparent to those skilled in the art. In the following description, specific details are set forth for purposes of explanation in order to provide a thorough understanding of embodiments of this application. However, it will be apparent, however, that various embodiments may be practiced without these specific details. These drawings and descriptions are not intended to be limiting.

[0029] The following description provides exemplary embodiments and is not intended to limit the scope, applicability, or configuration of this disclosure. Rather, the subsequent description of exemplary embodiments will provide those skilled in the art with a feasible description of how to implement the exemplary embodiments. It should be understood that various changes can be made to the function and arrangement of the elements without departing from the spirit and scope of the application described in the appended claims.

[0030] Many portable electronic devices, such as smartphones, tablets, and laptops, are capable of performing facial recognition. For example, portable electronic devices can use facial recognition to perform authentication, such as verifying user identity (e.g., checking whether the attempted device access is registered in an authorized user's database). Facial authentication has many applications, such as performing device access control, for example, "unlocking" access to the device to provide access to specific applications or services, etc.

[0031] In other examples, portable electronic devices may use facial recognition to implement display management features based on the user's attention to the device. For example, the device's forward sensors (e.g., a dot projector and / or a camera) may be used to facilitate facial recognition in order to initialize and / or maintain the device display, for example, as long as the user is looking at the screen. Other examples of device actions based on user attention include automatically changing display brightness, device "lock" timeouts, and / or adjusting alarm volume.

[0032] Some existing facial recognition systems use an infrared (IR) light source to illuminate the user's face and use an infrared (IR) camera to perform image capture. In some cases, the captured image can then be processed and compared with a stored list of registered faces to perform user authentication. While existing facial recognition systems are generally reliable, such systems can be power-intensive. To overcome this problem, facial recognition systems on wireless devices typically trigger only when some type of user activity or predetermined condition is detected, such as a screen tap, device movement, incoming notifications, etc. Without these triggers, the facial recognition system is disabled when the device is locked to conserve battery life. Therefore, existing systems have an inherent delay in performing facial recognition to authenticate the user and "unlock" the device.

[0033] The high power consumption of existing facial recognition systems also poses a problem when implementing user-perceived features for display management. Although these features may not require the accuracy needed for facial recognition systems used for facial authentication, the lack of more effective alternatives necessitates the use of existing systems, which adversely affects the battery life of the devices.

[0034] In addition to issues related to high power consumption, some facial recognition systems fail to function properly when exposed to direct light sources (such as sunlight) because strong incident light can interfere with the fidelity of infrared images. Another problem is that existing facial recognition systems may fail if the user is wearing glasses that filter or block infrared light. Furthermore, existing facial recognition systems are prone to fraudulent authentication based on the user's photograph.

[0035] It is desirable to develop a technology that enables devices to perform facial recognition, thereby reducing startup latency and improving power management to reduce overall power consumption and save battery life. Furthermore, it is desirable to develop a technology that overcomes problems associated with facial recognition under direct sunlight or using any type of glasses, and reduces the possibility of any false authentication. Moreover, it is preferable to utilize existing radio frequency (RF) interfaces on the device to implement these technologies.

[0036] This document describes systems, apparatuses, processes (also referred to as methods), and computer-readable media (collectively, “systems and techniques”) for performing facial recognition. While the systems and techniques described herein relate to facial recognition, they 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 the movement of users, animals, objects, etc.

[0037] Systems and technologies provide electronic devices with the ability to collect RF sensing data, which can be used to perform facial recognition, detect motion, determine the presence of a user's head and / or other body parts (e.g., part of the head, face, head / neck region, hands, eyes, etc.), determine the orientation of the user's face, and / or perform facial authentication. In some aspects, RF sensing data can be collected by utilizing a wireless interface capable of simultaneously performing transmission and reception functions (e.g., a monostatic configuration). In other aspects, RF sensing data can be collected by utilizing a bistatic configuration, where transmission and reception 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). Wi-Fi will be used here as an illustrative example to describe the example. However, systems and technologies are not limited to Wi-Fi. For example, in some cases, systems and technologies can be implemented using 5G / New Radio (NR), such as millimeter wave (mmWave) technology. In some cases, systems and technologies can be implemented using other wireless technologies, such as Bluetooth™, Ultra Wideband (UWB), etc.

[0038] In some aspects, the device may include a Wi-Fi interface configured to implement algorithms with different levels of RF sensing resolution based on the bandwidth of the transmitted RF signals, the number of spatial streams, the number of antennas configured to transmit RF signals, the number of antennas configured to receive RF signals, the number of spatial links (e.g., the number of spatial streams multiplied by the number of antennas configured to receive RF signals), the sampling rate, or any combination thereof. For example, the device's Wi-Fi interface may be configured to implement a low-resolution RF sensing algorithm that consumes little power and can run in the background when the device is in a "locked" state and / or in a "sleep" mode. In some cases, the device may use the low-resolution RF sensing algorithm as a coarse detection mechanism that can sense motion within a specific neighborhood of the device. In some aspects, the low-resolution RF sensing algorithm can be used as a trigger to initiate a facial recognition system of the device and can provide lower latency than existing triggers (e.g., device movement, screen taps, alarms, etc.). In some respects, using a low-resolution RF sensing algorithm to detect motion can trigger the device to execute a higher-resolution RF sensing algorithm (e.g., a medium-resolution RF sensing algorithm, a high-resolution RF sensing algorithm, or other higher-resolution RF sensing algorithms, as discussed herein) before initiating facial recognition.

[0039] In some examples, the device's Wi-Fi interface can be configured to implement a mid-resolution RF sensing algorithm. The transmitted RF signal used for the mid-resolution RF sensing algorithm can differ from that of the low-resolution RF sensing algorithm, having higher bandwidth, a higher number of spatial streams, a higher number of spatial links (e.g., a higher number of antennas configured to receive RF signals and / or a higher number of spatial streams), a higher sampling rate (corresponding to a smaller sampling interval), or any combination thereof. In some cases, the mid-resolution RF sensing algorithm can be used to detect the presence of a user's head (or other body parts, such as face, eyes, etc.) and motion near the device. In some examples, as described above, the mid-resolution RF sensing algorithm can be invoked in response to detecting motion near the device by using the low-resolution RF sensing algorithm. In some cases, the mid-resolution RF sensing algorithm can focus its detection on the user's head by utilizing digital signal processing to filter signals not reflected from the direction facing the device screen. In some examples, the mid-resolution RF sensing 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 motion, touchscreen interaction, alarms, etc.). In some cases, detecting the presence of a user's head using a medium-resolution RF sensing algorithm can trigger the device to execute a higher-resolution RF sensing algorithm (e.g., a high-resolution RF sensing algorithm or other higher-resolution RF sensing algorithms, as described herein) before initiating facial recognition.

[0040] In another example, the device's Wi-Fi interface can be configured to implement a high-resolution RF sensing algorithm. The transmitted RF signal used for the high-resolution RF sensing algorithm can differ from that of the medium-resolution and low-resolution RF sensing algorithms because it has higher bandwidth, a higher number of spatial streams, a higher number of spatial links (e.g., a higher number of antennas configured to receive RF signals and / or a higher number of spatial streams), a higher sampling rate, or any combination thereof. In some cases, the high-resolution RF sensing algorithm can be used to detect the orientation of a 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 near the device. In some examples, the high-resolution RF sensing algorithm can be invoked in response to detecting motion near the device and / or in response to detecting the presence of a user's head (or other body parts, such as the face, eyes, etc.). In some aspects, the high-resolution RF sensing algorithm can utilize digital signal processing to filter signals not reflected from the direction facing the device screen. In some cases, the high-resolution RF sensing algorithm can be used as a trigger to initiate a facial recognition system of the device and can provide lower latency than the existing facial recognition triggers described above.

[0041] In some examples, the device's Wi-Fi interface can be configured to implement a facial authentication RF sensing algorithm. In one implementation, the device can utilize an RF interface capable of transmitting extremely high frequency (EHF) signals or mmWave technology (e.g., IEEE 802.11ad) to perform facial recognition. For example, the device may include an mmWave RF interface. In some examples, the mmWave 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 mmWave RF interface to perform narrow beam scanning 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 facial features. The device can compare the facial features with calibrated facial measurements stored in the system for facial recognition.

[0042] Facial recognition implemented according to such systems and technologies can be advantageously used by users in direct sunlight or wearing infrared shading glasses. Furthermore, facial recognition based on these systems and technologies can incorporate three-dimensional data of the user's face, thus producing higher accuracy than existing systems.

[0043] In some examples, the system and techniques can perform RF sensing associated with each of the algorithms described above by implementing a Wi-Fi interface of a device with at least two antennas, which can be used to simultaneously transmit and receive RF signals. In some cases, the antennas can be omnidirectional, allowing RF signals to be received and transmitted from all directions. For example, the device can utilize the transmitter of its Wi-Fi interface to transmit RF signals and simultaneously enable the Wi-Fi receiver of the Wi-Fi interface so that the device can capture any signals reflected from the user. The Wi-Fi receiver can also be configured to detect leaked signals transmitted from the antenna of the Wi-Fi transmitter to the antenna of the Wi-Fi receiver without being reflected from any object. In doing so, the device can collect RF sensing data in the form of channel state information (CSI) data associated with the direct path of the transmitted signal (leaked signal) and data associated with the reflection path of the received signal corresponding to the transmitted signal.

[0044] In some respects, CSI data can be used to calculate the distance and angle of arrival of the reflected signal. The distance and angle of the reflected signal can be used to detect motion, 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 distance and angle of arrival of the reflected signal can be determined using signal processing, machine learning algorithms, any other suitable techniques, or any combination thereof. In one example, the distance of the reflected signal can be calculated by measuring the time difference from receiving the leaked signal to receiving the reflected signal. In another example, the angle of arrival can be calculated by using an antenna array to receive the reflected signal and measuring the received phase difference at each element of the antenna array. In some cases, the distance and angle of arrival of the reflected signal can be used to identify the presence and orientation characteristics of a user, for example, by identifying the presence and / or orientation of the user's head.

[0045] In some examples, one or more RF sensing algorithms discussed herein can be used to perform user-awareness-based device management functions. For example, one or more RF sensing algorithms can be used to determine the user's head orientation. The user's head orientation can then be used to infer whether the user is directing their attention toward the device screen or elsewhere. This implementation can result in lower power consumption compared to existing systems that utilize facial authentication to determine user awareness.

[0046] The various aspects of the systems and technologies described herein will be discussed below with reference to the accompanying drawings. Figure 1 illustrates an example of a computing system 170 of user equipment 107. User equipment 107 is an example of a device that can be used by an end user. For example, user equipment 107 may include a mobile phone, router, tablet computer, laptop computer, tracking device, wearable device (e.g., smartwatch, glasses, XR device, etc.), Internet of Things (IoT) device, vehicle (or computing device of a vehicle), and / or another device used by the user to communicate via a wireless communication network. In some cases, the device may be referred to as a site (STA), for example when referring to a device configured to communicate using a Wi-Fi standard. In some cases, the device may be referred to as user equipment (UE), for example when referring to a device configured to communicate using 5G / New Radio (NR), Long Term Evolution (LTE), or other telecommunications standards.

[0047] The computing system 170 includes software and hardware components that can be electrically or communicatively coupled (or communicate as needed) via bus 189. For example, the 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, dedicated hardware, any combination thereof, and / or other processing devices and / or systems. Bus 189 may be used by the one or more processors 184 for communication between cores and / or with one or more memory devices 186.

[0048] The computing system 170 may also include one or more storage devices 186, one or more digital signal processors (DSPs) 182, one or more subscriber identification 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., camera, mouse, keyboard, touch screen, touchpad, keyboard, microphone, etc.) and one or more output devices 180 (e.g., display, speaker, printer, etc.).

[0049] One or more wireless transceivers 178 may receive wireless signals (e.g., signal 188) from one or more other devices via antenna 187. These devices include other user equipment, network devices (e.g., base stations, such as eNBs and / or gNBs, WiFi access points (APs), such as routers, range extenders, etc.), cloud networks, etc. In some examples, computing system 170 may include multiple antennas or antenna arrays that facilitate simultaneous transmission and reception. Antenna 187 may be an omnidirectional antenna, allowing RF signals to be received and transmitted from all directions. Wireless signal 188 may be transmitted via a wireless network. The wireless network may be any wireless network, such as cellular or telecommunications networks (e.g., 3G, 4G, 5G, etc.), wireless local area networks (e.g., WiFi networks), Bluetooth™ networks, and / or other networks. In some examples, one or more wireless transceivers 178 may include an RF front end comprising one or more components, such as amplifiers, a mixer for down-converting signals (also called a signal multiplier), a frequency synthesizer (also called an oscillator) that supplies signals to the mixer, a baseband filter, an analog-to-digital converter (ADC), one or more power amplifiers, and other components. The RF front end typically handles the selection and conversion of the wireless signal 188 to baseband or intermediate frequency, and can convert the RF signal to the digital domain.

[0050] In some cases, the computing system 170 may include an encoding / 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, the 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., according to Advanced Encryption Standard (AES) and / or Data Encryption Standard (DES) standards).

[0051] One or more SIMs 174 may each securely store an International Mobile Subscriber Identity (IMSI) number and associated key assigned to a user of user equipment 107. The IMSI and key can be used to identify and authenticate subscribers when accessing a network provided by a network service provider or operator associated with one or more SIMs 174. One or more modems 176 may modulate one or more signals to encode information for transmission using one or more radio transceivers 178. One or more modems 176 may also demodulate signals received by one or more radio transceivers 178 to decode transmitted information. In some examples, one or more modems 176 may include a WiFi modem, a 4G (or LTE) modem, a 5G (or NR) modem, and / or other types of modems. One or more modems 176 and one or more radio transceivers 178 can be used to communicate data for one or more SIMs 174.

[0052] The computing system 170 may also include (and / communicate with) one or more non-transitory machine-readable storage media or storage devices (e.g., one or more storage devices 186), which may include, but are not limited to, local and / or network-accessible storage, disk drives, drive arrays, optical storage devices, a solid-state storage device, such as RAM and / or ROM, which may be programmable, flash memory updatable, and / or the like. Such storage devices may be configured to implement any suitable data storage, including but not limited to various file systems, database structures, etc.

[0053] In various embodiments, functionality may be stored as one or more computer program products (e.g., instructions or code) in storage device 186 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., residing within one or more storage devices 186) including, for example, operating systems, device drivers, executable libraries, and / or other code, such as one or more applications, which may include computer programs implementing the functionality provided in various embodiments, and / or may be designed to implement methods and / or configure systems as described herein.

[0054] Figure 2 is a diagram illustrating an example of a wireless device 200 that utilizes RF sensing technology to perform one or more functions, such as detecting the presence of a user 202, detecting the user's orientation features, performing facial recognition, any combination thereof, and / or performing other functions. In some examples, the wireless device 200 may be user equipment 107, such as a mobile phone, tablet computer, wearable device, or other device including at least one RF interface. In some examples, the wireless device 200 may be a device that provides connectivity to a user equipment (e.g., user equipment 107), such as a wireless access point (AP), a base station (e.g., gNB, eNB, etc.), or other device including at least one RF interface.

[0055] In some aspects, the wireless device 200 may include one or more components for transmitting RF signals. The wireless device 200 may include a digital-to-analog converter (DAC) 204 capable of receiving digital signals or waveforms (e.g., from a microprocessor, not shown) and converting the signals or waveforms into analog waveforms. The analog signal, as the output of the DAC 204, may be provided to the RF transmitter 206. The 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.

[0056] The RF transmitter 206 may be coupled to one or more transmitting antennas, such as the 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 that can radiate Wi-Fi signals in a 360-degree radiation pattern (e.g., 2.4 GHz, 5 GHz, 6 GHz, etc.). In another example, the TX antenna 212 may be a directional antenna that transmits RF signals in a specific direction.

[0057] In some examples, the wireless device 200 may also include one or more components for receiving RF signals. For example, the receiver array in the wireless device 200 may include one or more receiving antennas, such as RX antenna 214. In some examples, RX antenna 214 may be an omnidirectional antenna capable of receiving RF signals from multiple directions. In other examples, RX antenna 214 may be a directional antenna configured to receive signals from a specific direction. In further examples, both TX antenna 212 and RX antenna 214 may include multiple antennas (e.g., elements) configured as an antenna array.

[0058] The wireless device 200 may also include an RF receiver 210 coupled to the RX antenna 214. The RF receiver 210 may include one or more hardware components for receiving RF waveforms, such as Wi-Fi signals, Bluetooth™ signals, 5G / NR signals, or any other RF signals. The 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 waveforms into digital waveforms that can be provided to a processor such as a digital signal processor (not shown).

[0059] In one example, the wireless device 200 can implement RF sensing technology by transmitting a TX waveform 216 from the TX antenna 212. Although the TX waveform 216 is shown as a single line, in some cases, the TX waveform 216 can be transmitted in all directions through the omnidirectional TX antenna 212. In one example, the TX waveform 216 can 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., beacon transmission). In some examples, the TX waveform 216 can 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., 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).

[0060] In some examples, the TX waveform 216 may correspond to a 5G NR waveform, which is transmitted simultaneously or nearly simultaneously with the 5G NR data communication signal or the 5G NR control function signal. In some examples, the TX waveform 216 may 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 may 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 may be transmitted at different times and / or using different frequency resources).

[0061] In some aspects, one or more parameters associated with the TX waveform 216 may be modified, which may be used to improve or reduce the RF sensing resolution. These 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., the number of spatial streams multiplied by the number of antennas configured to receive RF signals), sampling rate, or any combination thereof.

[0062] In a further example, the TX waveform 216 can be implemented as a sequence with perfect or near-perfect autocorrelation characteristics. For example, the TX waveform 216 may include a single-carrier Zadoff sequence, or it may include symbols similar to orthogonal frequency division multiplexing (OFDM) long training field (LTF) symbols. In some cases, the TX waveform 216 may include a chirp signal, for example, used in frequency modulated continuous wave (FM-CW) radar systems. In some configurations, the chirp signal may include a signal in which the signal frequency increases and / or decreases periodically in a linear and / or exponential manner.

[0063] In some aspects, the wireless device 200 can further implement RF sensing technology by performing concurrent transmission and reception functions. For example, the wireless device 200 can enable its RF receiver 210 to receive simultaneously or nearly simultaneously with the RF transmitter 206 transmitting the TX waveform 216. In some examples, the transmission of a sequence or pattern included in the TX waveform 216 can be repeated continuously, such that the sequence is transmitted a certain number of times or for a certain duration. In some examples, if the RF receiver 210 is enabled after the RF transmitter 206, repeating the pattern in transmitting the TX waveform 216 can be used to avoid missing the reception of any reflected signals. In one example implementation, the TX waveform 216 may include a sequence having a sequence length L that is transmitted two or more times, which can allow the RF receiver 210 to be enabled for a time less than or equal to L in order to receive reflections corresponding to the entire sequence without losing any information.

[0064] By implementing simultaneous transmission and reception, wireless device 200 can receive any signal corresponding to TX waveform 216. For example, wireless device 200 can receive signals reflected from objects or people within the range of TX waveform 216, such as RX waveform 218 reflected from user 202. Wireless device 200 can also receive leaked signals (e.g., TX leaked signal 220) directly coupled from TX antenna 212 to RX antenna 214 without reflection from any object. For example, leaked signals may include signals transmitted from a transmitter antenna (e.g., TX antenna 212) on the wireless device to a receiver antenna (e.g., RX antenna 214) on the wireless device without reflection from any object. In some cases, RX waveform 218 may include multiple sequences corresponding to multiple copies of the sequence included in TX waveform 216. In some examples, wireless device 200 may combine multiple sequences received by RF receiver 210 to improve signal-to-noise ratio (SNR).

[0065] The wireless device 200 can further implement RF sensing technology 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 associated with the direct path of the TX waveform 216 (e.g., leakage signal 220) and data associated with the reflection path corresponding to the TX waveform 216 (e.g., RX waveform 218).

[0066] In some aspects, RF sensing data (e.g., CSI data) may include information that can be used to determine how an RF signal (e.g., TX waveform 216) propagates from RF transmitter 206 to RF receiver 210. The RF sensing data may include data corresponding to the effects on the transmitted RF signal due to scattering, fading, and / or power attenuation with distance, or any combination thereof. In some examples, the RF sensing data may include virtual data and real data (e.g., I / Q components) corresponding to each tone in the frequency domain over a specific bandwidth.

[0067] In some examples, RF sensing data can be used to calculate the distance and angle of arrival corresponding to the reflected waveform (such as RX waveform 218). In further examples, 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) in the surrounding environment in order to detect the user's presence / proximity, detect the user's attention, and / or perform facial recognition and user authentication (e.g., facial authentication).

[0068] Wireless device 200 can calculate the distance and angle of arrival corresponding to the reflected waveform (e.g., the distance and angle of arrival corresponding to RX waveform 218) by utilizing signal processing, machine learning algorithms, using any other suitable techniques, or any combination thereof. In other examples, wireless device 200 can transmit RF sensing data to another computing device, such as a server, which can perform calculations to obtain the distance and angle of arrival corresponding to RX waveform 218 or other reflected waveforms.

[0069] In one example, the distance of the RX waveform 218 can be calculated by measuring the time difference from receiving the leaked signal to receiving the reflected signal. For example, wireless device 200 can determine a zero baseline distance based on the difference (e.g., propagation delay) between the time wireless device 200 transmits the TX waveform 216 and the time it receives the leaked signal 220. 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 wireless device 200 transmits the TX waveform 216 and the time it receives the RX waveform 218, and can then adjust that distance according to the propagation delay associated with the leaked signal 220. In this way, wireless device 200 can determine the distance traveled by the RX waveform 218, which can be used to determine the presence and actions of the user (e.g., user 202) that caused the reflection.

[0070] 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 the individual elements of the receiving antenna array (e.g., antenna 214). In some examples, the time difference of arrival can be calculated by measuring the receiving phase difference of each element in the receiving antenna array.

[0071] In some cases, the distance and angle of arrival of the RX waveform 218 can be used to determine the distance between the wireless device 200 and the user 202, as well as the position of the user 202 relative to the wireless device 200. The distance and angle of arrival of the RX waveform 218 can also be used to determine the presence, movement, proximity, attention, identity, or any combination thereof of the user 202. For example, the wireless device 200 can use the calculated distance and angle of arrival corresponding to the RX waveform 218 to determine that the user 202 is walking toward the wireless device 200. Based on the proximity of the user 202 to the wireless device 200, the wireless device 200 can initiate facial authentication to unlock the device. In some aspects, facial authentication can be initiated based on the user 202 being within a threshold distance to the wireless device 200. Examples of threshold distances include 2 feet, 1 foot, 6 inches, 3 inches, or any other distance.

[0072] As described above, the wireless device 200 may include mobile devices (e.g., smartphones, laptops, tablets, etc.) or other types of devices. In some examples, the wireless device 200 may be configured to acquire device location data and device orientation data together with RF sensing data. In some cases, the device location data and device orientation data may be used to determine or adjust the distance and angle of arrival of the reflected signal (e.g., RX waveform 218). For example, when user 202 walks toward the wireless device 200 during RF sensing, the wireless device 200 may be placed on a table facing the ceiling. In this case, the wireless device 200 may use its location data and orientation data, along with the RF sensing data, to determine the direction in which user 202 is walking.

[0073] In some examples, the wireless device 200 may use techniques including round-trip time (RTT) measurement, passive positioning, angle of arrival, received signal strength indicator (RSSI), CSI data, or any other suitable technique or any combination thereof to collect device location data. In further examples, device orientation data may be obtained from electronic sensors on the wireless device 200, such as gyroscopes, accelerometers, compasses, magnetometers, barometers, any other suitable sensors, or any combination thereof.

[0074] Figure 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 user equipment (e.g., user equipment 107 of Figure 1, such as a mobile device or any other type of device). In some examples, the AP 304 may also be referred to as a wireless device. As shown, the user 308 may move to different locations (e.g., together 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, the wireless device 302 and the AP 304 may each be configured to perform RF sensing to detect the presence of the user 308, detect the movement of the user 308, perform facial recognition of the user 308, any combination thereof, and / or perform other functions relating to the user 308.

[0075] In some aspects, AP 304 may be a Wi-Fi access point, which includes hardware and software components that can be configured to simultaneously transmit and receive RF signals, such as those described herein with respect to wireless device 200 of FIG2. For example, AP 304 may include one or more antennas configurable to transmit RF signals and one or more antennas configurable to receive RF signals (e.g., antenna 306). As described with respect to wireless device 200 of FIG2, AP 304 may include an omnidirectional antenna or antenna array configured to transmit and receive signals from any direction.

[0076] In some aspects, AP 304 and wireless device 302 can be configured to implement a bistatic configuration, wherein transmitting and receiving functions are performed by different devices. For example, AP 304 can transmit an omnidirectional RF signal that may include signal 310a and signal 310b. As shown, signal 310a can be transmitted directly from AP 304 (e.g., without reflection) to wireless device 302, and signal 310b can be reflected from user 308 at location 309a, causing wireless device 302 to receive the corresponding reflected signal 312.

[0077] In some examples, wireless device 302 may utilize RF sensing data associated with signals 310a and 310b to determine the presence, location, orientation, and / or movement of user 308 at location 309a. For example, wireless device 302 may acquire, retrieve, and / or estimate location data associated with AP 304. In some aspects, wireless device 302 may use location data and RF sensing data (e.g., CSI data) associated with AP 304 to determine signals transmitted by AP 304 that are associated with time of flight, distance, and / or angle of arrival (e.g., direct path signals such as signal 310a and reflected path signals such as signal 312). In some cases, AP 304 and wireless device 302 may further transmit and / or receive communications that may include data associated with RF signal 310a and / or reflected signal 312 (e.g., transmission time, sequence / pattern, time of arrival, angle of arrival, etc.).

[0078] In some examples, wireless device 302 may be configured to perform RF sensing using a monobase configuration, in which case wireless device 302 performs both transmit and receive functions simultaneously (e.g., synchronous TX / RX discussed in conjunction with wireless device 200). For example, wireless device 302 may detect the presence or movement of user 308 at location 309b by transmitting RF signal 314, which may enable wireless device 302 to receive reflected signal 316 from user 308 at location 309b.

[0079] In some aspects, the wireless device 302 may obtain RF sensing data associated with 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 the distance and angle of arrival corresponding to the reflected signal 316. For example, the wireless device 302 may determine the distance by calculating the time of flight of the reflected signal 316 based on the difference between the leaked signal (not shown) and the reflected signal 316. In a further example, the wireless device 302 may determine the angle of arrival by using an antenna array to receive the reflected signal and measuring the received phase difference at each element of the antenna array.

[0080] In some examples, the wireless device 302 can obtain RF sensing data in the form of CSI data, which can be used to form a matrix based on the number of frequencies (e.g., tone) denoted as "K" and the number of antenna array elements denoted as "N". In one technique, the CSI matrix can be represented according to the relationship given by equation (1):

[0081] (1)

[0082] When forming the CSI matrix, the wireless device 302 can calculate the angle of arrival and time of flight of the direct signal path (e.g., the leaked 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 multiple tones in the frequency domain; N corresponds to multiple receiving antennas; hik corresponds to the CSI data captured on the i-th antenna and the k-th tone (e.g., a complex number with real and imaginary parts); f0 corresponds to the carrier frequency; l corresponds to the antenna spacing; c corresponds to the speed of light; and ∆f corresponds to the frequency interval between two adjacent tones. The relationship of equation (2) is as follows:

[0083] (2)

[0084] In some respects, leakage signals (e.g., leakage signal 220 and / or other leakage signals) can be eliminated by using an iterative elimination method.

[0085] In some cases, the wireless device 302 can detect the presence or movement of the user 308 at location 309b using the distance and angle of arrival corresponding to the reflected signal 316. 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, which 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 facial authentication.

[0086] In some embodiments, the wireless device 302 may utilize artificial intelligence or machine learning algorithms to perform motion detection, object classification, and / or head orientation detection associated with the user 308. In some examples, machine learning techniques may include supervised machine learning techniques, such as those 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 sample RF sensing dataset may be selected to train the machine learning algorithm or artificial intelligence.

[0087] In some aspects, wireless device 302 and AP 304 can perform RF sensing techniques regardless of their association with each other or with a Wi-Fi network. For example, when wireless device 302 is not associated with any access point or Wi-Fi network, wireless device 302 can utilize its Wi-Fi transmitter and Wi-Fi receiver to perform the RF sensing discussed herein. In a further example, AP 304 can perform RF sensing techniques regardless of whether it has any wireless devices associated with it.

[0088] Figure 4 is a flowchart illustrating an example of a process 400 for performing facial recognition. Block 402 shows the device state when the device is locked and the device's screen (or display) is off. For example, the user may have placed the device on a table, and after a period of inactivity, the device may have transitioned to a locked state.

[0089] At block 404, the device can perform RF sensing to detect motion in the vicinity of the device. In one example, motion detection can be achieved by configuring the RF interface on the device to perform simultaneous transmission and reception (similar to what has been described above, e.g., with respect to wireless device 200 in Figure 2). For example, the Wi-Fi 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.

[0090] In some embodiments, the device may be configured to implement an RF sensing algorithm with different levels of RF sensing resolution based on parameters such as the bandwidth of the transmitted RF signal, the number of spatial streams, the number of antennas configured to transmit RF signals, the number of antennas configured to receive RF signals, the number of spatial links (e.g., the number of spatial streams multiplied by the number of antennas configured to receive RF signals), the sampling rate, or any combination thereof. For example, the device may implement an algorithm that, when the device is locked or in a sleep state, can detect motion near the device by adjusting one or more parameters related to the bandwidth, sampling rate, and / or spatial links.

[0091] For example, in some cases, the device may be configured to transmit using spatial streaming or multiplexing techniques that allow the transmission of independently and individually encoded signals (e.g., streams) from each transmit antenna. For example, a wireless device with four antennas can be configured to implement a 1x3 configuration (e.g., one spatial stream and three RX antennas, which would result in three spatial links) by configuring one antenna to transmit and the remaining three antennas to receive (e.g., in this case, one TX antenna can transmit a spatial stream that can be received by the other three RX antennas). In another example, the wireless device can implement a 2x2 configuration (e.g., two spatial streams and two RX antennas, which would result in four spatial links) by transmitting independent signals via two antennas configured to transmit, which are received by two antennas configured to receive.

[0092] In some configurations, the device can adjust the level of RF sensing resolution by modifying the number of spatial links (e.g., adjusting the number of spatial streams and / or the number of receiving antennas) as well as the bandwidth and sampling frequency. In some cases, the device can implement a low-resolution RF sensing algorithm (e.g., with relatively low bandwidth, low number of spatial links, and low sampling rate), which consumes little power and can run in the background when the device is locked or in sleep mode. In one example, the device can be configured to transmit a signal with a bandwidth of approximately 20 MHz using a single spatial link and perform motion detection by 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 can be implemented using different variations of the parameters and values.

[0093] In block 406, the device can determine whether motion has been detected based on RF sensing data. The device can detect motion by utilizing signal processing, machine learning algorithms, using any other suitable technology, or any combination thereof. If no motion is detected, process 400 can continue to block 408, where the device remains locked and continues to perform RF sensing to detect motion. In this case, the device can continue to perform RF sensing using a low-resolution RF sensing algorithm.

[0094] If motion is detected at block 406, process 400 can proceed to block 410 and initiate facial authentication. In some examples, facial recognition can be performed using an RF interface capable of transmitting extremely high frequency (EHF) signals or mmWave technology (e.g., IEEE 802.11ad), for example, by transmitting signals in a direction perpendicular to the device screen. For example, the device can utilize an mmWave RF interface to perform narrow-beam scanning to obtain time-of-flight and phase measurements based on different angles of the signal 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, which can be compared with calibrated facial measurements stored in the system for facial recognition.

[0095] In another example, the device may use Light Detection and Ranging (LIDAR) to perform facial recognition. For example, a LIDAR device may be used to illuminate a user's face with a laser and measure the time required for the laser to reflect back to the sensor. In some cases, the device may use the difference in return time and / or wavelength to generate a three-dimensional representation or image of the user's face, which may be used to perform facial recognition.

[0096] In another example, the device may use an infrared (IR) light source, a dot projector, or other light source to illuminate the user's face and use an IR camera or other image capture device to perform image capture. Then, at block 410, the captured image may be processed and used to perform facial authentication. For example, the captured image or captured image data may be compared with a stored / registered face or corresponding signature to perform authentication. If process 400 determines that facial authentication has failed at block 412, process 400 may continue to block 408, where the device continues to perform RF sensing to detect motion. If facial authentication is verified at block 412, process 400 may continue to block 414, where the device is unlocked.

[0097] Figure 5 is a flowchart illustrating an example of a process 500 for performing facial recognition. Block 502 shows the device state when the device is locked and the device's screen (or display) is off. For example, the user may have placed the device on a table, and / or the device may enter a locked state after a period of inactivity.

[0098] At block 504, the device can perform RF sensing to detect the presence of a user near the device 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 a user's head. In some implementations, RF sensing to detect the presence of a user may be performed in response to motion detection, as discussed with respect to process 400. For example, the device may implement RF sensing by using parameters that provide low-power operation for motion detection (e.g., bandwidth, sampling rate, spatial flow, spatial link, any combination thereof, and / or other parameters). In response to motion detection, the device may implement RF sensing using different sets of parameters that can be configured to detect the presence of a user (e.g., different bandwidths, different sampling rates, different spatial flows, different spatial links, etc.).

[0099] In one example, a device placed on a table in a room can use the techniques discussed in process 400 to detect that a user is moving around the room but has not yet approached (e.g., within a threshold distance from which the user can operate the device). Upon detecting that a user is moving toward the device, the device can implement different RF sensing algorithms (e.g., according to process 500 in Figure 5), which can be configured to detect the presence of the user by detecting and recognizing the user's head.

[0100] RF sensing for detecting the presence of a user can be implemented by configuring an RF interface on the device to perform both transmitting and receiving functions simultaneously. For example, the RF interface on the device can be configured to transmit one or more RF signals and simultaneously receive one or more reflected signals corresponding to the transmitted RF signals.

[0101] As described above, in some embodiments, the device may be configured to implement RF sensing algorithms with different levels of RF sensing resolution based on parameters (e.g., 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, when the device is locked or in sleep mode, the device may implement an algorithm capable of detecting the presence of a user (e.g., by performing head detection to detect the presence of a head) by adjusting one or more parameters related to bandwidth, sampling rate, spatial streams, and / or spatial links. In some cases, the device may implement a medium-resolution RF sensing algorithm (e.g., with medium bandwidth, a medium number of spatial links, and a medium sampling rate compared to a low-resolution RF sensing algorithm), which may differ from a low-resolution RF sensing algorithm by having higher bandwidth, a higher number of spatial links, a higher sampling rate, or any combination thereof. For example, compared to the parameters used for motion detection in process 400 of FIG4, the device may increase bandwidth, increase the sampling rate (to collect more samples), and / or increase the number of spatial links to detect the presence of a user. In an illustrative example, the device can be configured with an RF interface to utilize two spatial links and transmit a signal with a bandwidth of approximately 40 MHz, and to detect user presence (e.g., head detection) by utilizing a sampling rate of approximately 50 ms. As discussed with respect to process 400, those skilled in the art will understand that the parameters and corresponding values ​​described herein are provided as example configurations, and the disclosed systems and techniques can be implemented using different variations of the parameters and values.

[0102] In block 506, the device can determine whether a user presence is detected based on RF sensing data. The device can detect the user presence by utilizing signal processing, machine learning algorithms, any other suitable techniques, or any combination thereof. If no user presence is detected (e.g., no head detection), process 500 can proceed to block 508, where the device remains locked and continues RF sensing to detect user presence (e.g., head presence) using process 500 of FIG. 5, or to detect motion using process 400 of FIG. 4. In this case, the device can continue RF sensing using a medium-resolution RF sensing algorithm.

[0103] If the presence of a user is detected at block 506, process 500 may continue to block 510 and initiate facial authentication. In some examples, facial authentication may be performed using RF sensing technology, by capturing images using the device's IR camera, by using a LIDAR sensor, or any combination thereof. Further details related to facial authentication are illustrated in the description of Figure 8.

[0104] If facial authentication fails at block 512, process 500 may continue to block 508, where the device continues to perform RF sensing to detect the user's presence. In some configurations, the process may alternatively perform RF sensing to detect motion, as described in conjunction with process 400. If facial authentication is verified at block 512, process 500 may continue to block 514, where the device is unlocked.

[0105] Figure 6 is a flowchart illustrating an example of a process 600 for performing facial recognition. Block 602 shows the device state when the device is locked and the device's screen (or display) is off. Similar to the examples in Figures 4 and 5, the user may have placed the device on a table, and / or the device may have entered a locked state after a period of inactivity.

[0106] At block 604, the device can perform RF sensing to determine whether a user is facing the device (e.g., based on head orientation). In some implementations, as discussed with respect to process 500, RF sensing for determining the user's head orientation can be performed in response to the detection of the user's head. For example, the device can implement RF sensing by using parameters (e.g., bandwidth, sampling rate, spatial flow, spatial link, any combination thereof, and / or other parameters) configured to detect the presence of a user (e.g., head presence) while minimizing power consumption. In response to detecting the presence of a user, the device can implement RF sensing using different sets of parameters (e.g., bandwidth, sampling rate, spatial flow, spatial link, any combination thereof, and / or other parameters) that can be configured to determine the user's head orientation.

[0107] In one example, a device placed on a table in a room can use the techniques discussed with respect to process 500 of Figure 5 to determine if a user's head is close to the device. In some aspects, if the user's head is within a threshold distance of the device (e.g., within 2 feet), the user's head is considered very close to the device. The presence of the user and the detection of a head within the threshold distance can then trigger RF sensing techniques to determine the orientation of the user's head. For example, RF sensing can determine whether the user is facing the device, or whether the device is on the user's lap when the user's attention is diverted elsewhere.

[0108] By configuring an RF interface on the device to perform both transmitting and receiving functions simultaneously, RF sensing for detecting head orientation can be achieved. For example, the RF interface on the device can be configured to transmit one or more RF signals and simultaneously receive one or more reflected signals corresponding to the transmitted RF signals.

[0109] Similarly, the device can be configured to implement RF sensing algorithms with different levels of RF sensing 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, the device can implement an algorithm that can detect the head orientation of a user when the user is locked or asleep by adjusting one or more parameters related to bandwidth, sampling rate, and / or spatial links. In some cases, the device can implement a high-resolution RF sensing algorithm (e.g., with high bandwidth, a high number of spatial links, and a high sampling rate compared to a medium-resolution RF sensing algorithm). A high-resolution RF sensing algorithm can differ from a medium-resolution RF sensing algorithm by having higher bandwidth, a higher number of spatial links, a higher sampling rate, or any combination thereof. For example, compared to the parameters used to detect the presence of a head in process 500 of Figure 5, the device can increase bandwidth, increase the sampling rate (to collect more samples), and / or increase the number of spatial links to detect head orientation. In one illustrative example, the device can be configured with an RF interface to transmit signals with a bandwidth of 80–160 MHz using three or more spatial links, and to detect head orientation using a sampling rate of less than 50 ms. As discussed with respect to the foregoing process, those skilled in the art will understand that the parameters and corresponding values ​​described herein are provided as example configurations, and the disclosed systems and techniques can be implemented using different variations of the parameters and values.

[0110] In block 606, the device can determine whether the user's head is facing the device based on RF sensing data. The device can detect the user's head orientation by utilizing signal processing, machine learning algorithms, using any other suitable techniques, or any combination thereof. If the device determines that the user's head is not facing the device, process 600 can continue to block 608, in which the device remains in a locked state and continues to perform RF sensing in order to detect the head orientation using process 600 of FIG. 6, detect the presence of the user (e.g., the presence of the head) using process 500 of FIG. 5, or detect motion using process 400 of FIG. 4. In this case, the device can continue to perform RF sensing using a high-resolution RF sensing algorithm.

[0111] In some examples, if the device determines at block 606 that the user's head orientation is not towards the device, the device can determine the user's orientation toward another device. In some aspects, the device can use any other suitable technology to implement a positioning algorithm (e.g., round-trip time (RTT) measurement, passive positioning, angle of arrival, received signal strength indicator (RSSI), CSI data), or the device can access location data (e.g., previously stored location data or location data from a server) to determine the range or distance to another device. In some cases, the device can use location data and device orientation data (e.g., from a gyroscope) to determine that the user's head orientation is toward another device. In an illustrative example, the device can determine that the user is facing a television, and the device can communicate with the television to turn it on.

[0112] If, at block 606, the device determines that the user's head is facing the device, then process 600 can proceed to block 610 and initiate facial authentication. In some examples, facial authentication can be performed using RF sensing technology, by capturing images using the device's infrared (IR) camera, by using a LiDAR sensor, or any combination thereof. Further details related to facial authentication are illustrated in the description of Figure 8.

[0113] If facial authentication fails at block 612, process 600 can proceed to block 608, where the device continues to perform RF sensing to detect the user's head orientation. In some configurations, the process may alternatively perform RF sensing to detect motion or user presence, as discussed in conjunction with processes 400 and 500 respectively. If facial authentication is verified at block 612, process 600 can proceed to block 614, where the device is unlocked.

[0114] Figure 7 is a flowchart illustrating an example of a process 700 for performing facial recognition. Block 702 shows the device state when the device is locked and the device's screen (or display) is off. Similar to the examples in Figures 4, 5, and 6, the user may have placed the device on a table, and / or the device may have entered a locked state after a period of inactivity.

[0115] In block 704, the device can execute a low-resolution RF sensing algorithm to detect motion near the device (e.g., detecting a user walking in the same room as the device). In one example, motion detection can be achieved by configuring the RF interface on the device to perform simultaneous transmission and reception (similar to what has been described above, e.g., with respect to wireless device 200 in Figure 2). For example, the Wi-Fi 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. In an illustrative example, the low-resolution RF sensing algorithm can be achieved by configuring the RF interface to transmit a signal with a bandwidth of approximately 20 MHz using a single spatial link and by utilizing a sampling rate that can be in the range of 100 ms to 500 ms.

[0116] In block 706, the device can determine whether motion is detected based on RF sensing data. If no motion is detected, process 700 can return to block 704, in which the device remains in a locked state and continues to perform low-resolution RF sensing to detect motion.

[0117] If motion is detected at block 706, process 700 can continue to block 708, and the device can execute a medium-resolution RF sensing algorithm to detect the presence of a user near the device 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 a user's head. The difference between a medium-resolution RF sensing algorithm and a low-resolution RF sensing algorithm is that it has higher bandwidth, more spatial links, a higher sampling rate, or any combination thereof. In an illustrative example, the device can detect the presence of a user (e.g., head detection) by configuring the RF interface to utilize two spatial links and transmit a signal with a bandwidth of approximately 40 MHz, and by utilizing a sampling rate of approximately 50 ms.

[0118] In block 710, the device can determine whether a user presence is detected based on RF sensing data. If no user presence is detected, process 700 can continue implementing the medium-resolution RF sensing algorithm at block 712. In some examples, the medium-resolution RF sensing algorithm can be used to detect motion at block 706. If no motion is detected, process 700 can return to block 704, where the device remains locked and continues to perform low-resolution RF sensing to detect motion.

[0119] If the presence of a user is detected at block 710, process 700 can continue to block 714, and the device can execute a high-resolution RF sensing algorithm to determine whether the user is facing the device (e.g., based on head orientation). High-resolution RF sensing algorithms can differ from medium-resolution RF sensing algorithms by having higher bandwidth, a higher number of spatial links, a higher sampling rate, or any combination thereof. In an illustrative example, the device can detect head orientation by configuring the RF interface to utilize three or more spatial links, transmit signals with a bandwidth of 80–160 MHz, and utilize a sampling rate of less than 50 ms.

[0120] At block 716, the device can determine whether the user is facing the device based on RF sensing data. If the user is not facing the device, process 700 can continue implementing the high-resolution RF sensing algorithm at block 718. In some examples, the high-resolution RF sensing algorithm can be used to detect motion at block 706. If no motion is detected, process 700 can return to block 704, where the device remains locked and continues to perform low-resolution RF sensing to detect motion. If at block 716 the device determines that the user's head is facing the device, process 700 can continue to block 720 and initiate facial authentication.

[0121] Figure 8 is a flowchart illustrating an example of a general authentication process 800 using a face as biometric data. An input image 802 of a user attempting to access the device is acquired. For example, the input image 802 may be an image compiled using RF sensing technology. In one example, the device may utilize an RF interface capable of transmitting extremely high frequency (EHF) signals or mmWave technology (e.g., IEEE 802.11ad) to transmit signals in an orientation perpendicular to the device screen. For example, the device may utilize an mmWave RF interface to perform narrow-beam scanning to obtain time-of-flight and phase measurements based on different angles of the signal 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, which can be compared with calibrated facial measurements stored in the system for facial recognition. In another example, the input image 802 may be acquired via a camera of a wireless device (e.g., input device 172). In yet another example, the input image 802 may be acquired using a LIDAR sensor of a wireless device (e.g., communication interface 1240).

[0122] In block 804, the input image 802 is processed for feature extraction. For example, in block 804, a feature representation including one or more features of the face can be extracted from the input image 802 containing a face. The feature representation of the face can be compared with a facial representation of a person authorized to access the device (e.g., stored as a template in template memory 808). In some examples, template memory 808 may include a database. In some examples, template memory 808 is part of the same device performing facial authentication (e.g., user device 107, wireless device 200, or other device). In some examples, template memory 808 may be located remotely to the device performing facial authentication (e.g., wireless device 200) (e.g., on a remote server communicating with the device).

[0123] Templates in template memory 808 can be generated during the registration step when a person registers their biometrics for later use during authentication. Each template can be internally (e.g., in template memory 808) linked to a subject identifier (ID) unique to the registrant. For example, during registration (also known 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., images, fingerprint samples, voice samples, or other biometric data). A feature extraction engine can extract representative features from the biometric data. Representative features of the biometric data can be stored as one or more templates in template memory 808. For example, multiple images of the owner or user can be captured using different poses, positions, facial expressions, lighting conditions, and / or other features.

[0124] In another example, RF sensing technology can be used to capture several different facial features. Facial features from different images or signatures can be extracted and saved as templates. For example, a template can be stored for each image / signature, where each template represents the features of each face with its unique pose, position, facial expression, lighting conditions, etc. One or more templates stored in template memory 808 can be used as reference points for performing facial authentication.

[0125] As described above, in block 804, one or more facial features can be extracted from the input image 802. Any suitable feature extraction technique can be used to extract features from biometric data (during registration and authentication). An example of a feature extraction process that can generate deep learning features is feature extraction based on neural networks (e.g., using deep learning networks). For example, a neural network can be trained using multiple training images to learn different features of various faces. In one configuration, the neural network can be trained using RF sensing data corresponding to features associated with an RF facial signature. Once trained, the trained neural network can be applied to the input image 802, including the face. The trained neural network can extract or determine facial features. The neural network can be a classification network, including hidden convolutional layers that apply kernels (also called filters) to the input image to extract features.

[0126] In block 806, the similarity between the user's feature representation extracted from input image 802 and the human facial feature representation stored in template memory 808 can be calculated. For example, the feature representation extracted from input image 802 can be compared with one or more templates stored in template memory 808. For example, in block 806, process 800 can perform a similarity calculation to calculate the similarity between input image 802 and one or more templates in template memory 808. The calculated similarity can be used as a similarity score 807 to be used to make a final authentication decision.

[0127] In some cases, the data in the input image 802 may also be referred to as query data (e.g., query face). In some cases, the template may also be referred to as registration data (e.g., register face). As described above, in some examples, features extracted for a face (or other object or biometric feature) can be represented using feature vectors representing a face (or other object or biometric feature). For example, each template can be a feature vector. The representation of features extracted from the input biometric data can also be a feature vector. Each feature vector can include multiple values ​​representing the extracted features. The values ​​of the feature vectors can include any suitable values. In some cases, the values ​​of the feature vectors can be floating-point numbers between -1 and 1, which are normalized feature vector values. The feature vectors representing facial features from the input image 802 can be compared or matched with one or more feature vectors of one or more templates to determine the similarity between the feature vectors. For example, similarity can be determined between the feature vectors representing faces in the input image 802 and the feature vectors of each template, resulting in multiple similarity values.

[0128] In some embodiments, the similarity between features of the registered face of the template (from template memory 808) and features of the query face (from input image 802) can be measured by 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 distances. In an illustrative example, the similarity between two faces can be calculated as the sum of the similarities of the two faces. In some cases, the sum of similarities can be based on the sum of absolute differences (SAD) between the query face (from input image 802) and the registered face of the template (from template memory 808).

[0129] One way to represent similarity is to use a similarity score (also known as a match score). A similarity score represents the similarity between features (indicating the degree of feature matching), where a higher score between two feature vectors indicates that the two feature vectors are more similar than the two feature vectors with a lower score. Referring to Figure 8, a similarity score 807 indicates the similarity between features of one or more stored templates and facial features extracted from the input image 802. The device can compare the similarity score 807 to one or more thresholds. In some cases, a similarity score can be determined between the query face (of the input image 802) and each registered face (corresponding to each template). The highest similarity score (corresponding to the best match) can be used as the similarity score 807.

[0130] In some examples, a similarity score can be generated based on the distance between facial features extracted from the input image 802 and the template data, or based on any other comparison metric. As previously mentioned, the distance can include cosine distance, Euclidean distance, Manhattan distance, Mahalanobis distance, absolute difference, Hadamard product, polynomial mapping, element-wise multiplication, and / or other suitable distances. As described above, a feature vector of the face can be generated based on feature extraction performed by the feature extraction engine. A similarity score between the face in the input image 802 and the template data can 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 data values ​​of the feature vector representing the template data. For example, cosine distance measures the cosine of the angle between two non-zero vectors in the inner product space. Cosine similarity represents a similarity measure between two non-zero vectors.

[0131] In some cases, the calculated distance (e.g., cosine distance, Euclidean distance, and / or other distances) can be normalized to 0 or 1. For example, the similarity score can be defined as 1000 * (1 - distance). In other cases, the similarity score can be a value between 0 and 1.

[0132] 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 with a similarity threshold. In some examples, the similarity threshold may include a percentage of similarity (e.g., 75%, 80%, 85% similarity of features, etc.). 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.

[0133] In some aspects, the authentication process 800 can be implemented using two or more similarity thresholds. For example, if the similarity score 807 is greater than the "high" threshold, the device can be unlocked at square 812. In another example, if the similarity score 807 is less than the "low" threshold, the device can remain locked. In some cases, if the similarity score 807 is less than the "low" threshold, an alternative unlocking mechanism (e.g., fingerprint scanning, access code, etc.) can be presented to the user. In some examples, a similarity score 807 less than the "high" threshold (e.g., between the "high" and "low" thresholds) can cause the device to prompt the user to perform a new facial scan (e.g., using the RF sensing technology described herein).

[0134] In some implementations, devices utilizing facial authentication (e.g., mobile devices such as smartphones) implement an unlock timeout. The unlock timeout refers to a period of time during which the device is inactive (when unlocked), after which it automatically locks and requires new facial authentication to unlock. In some examples, such devices may also implement a separate screen timeout. The screen timeout refers to the period during which the device is inactive (when the device's screen or display is active or "on"), after which the device's screen or display automatically turns off (e.g., the screen or display loses power). The device may remain unlocked while the screen or display is off.

[0135] Figure 9 is a flowchart illustrating an example of a device management process 900 performed based on the user's attention. Block 902 shows the device state when the device is unlocked and the device's screen (or display) is turned on. For example, the user may have provided authentication information (e.g., facial recognition, fingerprint authentication, access code, etc.) to gain access to the device and subsequently begun using the device.

[0136] At block 904, the device may perform RF sensing to determine whether the user's attention is still directed at the device. For example, the device may be configured to perform one or more operations associated with RF sensing, as described in conjunction with process 500 for determining user presence (e.g., head detection), and / or one or more operations associated with RF sensing, as described in conjunction with process 600 for determining user head orientation. In some cases, the device may be configured to perform one or more operations associated with RF sensing, as described in conjunction with process 400 for determining motion (e.g., before performing the operations of process 500).

[0137] At block 906, the device can use RF sensing data to determine whether a user's head has been detected. If no head is detected, the device can determine that the user is no longer using the device and can proceed to block 908, which locks the device. In some configurations, the device can implement a timer before entering block 908. For example, if no user's head is detected within a certain period of time (e.g., 2 minutes), the device can determine that the user no longer exists and can lock access to the device.

[0138] At block 906, if the device determines the presence of a user (e.g., detects a head), the process can continue to block 910 to determine the user's head orientation. For example, the user may be holding the device but talking to another person, causing the user's face to be not facing the device. In this case, the process can continue to block 912 and dim the display backlight to save power and / or battery life. In some configurations, the device can continue to block 912 after a period of inattention (e.g., the user's head orientation has shifted for 2 minutes).

[0139] At block 910, if the device determines that the user is facing the device, the process can continue to block 914 and adjust one or more settings on the device. For example, the device may detect that the display backlight previously dimmed (e.g., at block 912) and should be increased because the user's attention is now directed at the device. In another example, the device may adjust the alarm volume (e.g., decrease the alarm volume) in response to determining that the user's attention is directed at the device. For example, the device's ringtone volume may be decreased in response to determining that the user is looking at the device screen. After adjusting the device settings, the process can return to block 904 and continue performing RF sensing to determine the user's attention.

[0140] Figure 10 is a flowchart illustrating an example of a process 1000 for performing facial recognition. In operation 1002, process 1000 includes receiving a first received waveform as a reflection of a first RF waveform by a first wireless device. In some examples, the first RF waveform is transmitted by the same device (via the first wireless device) that receives the first received waveform (e.g., a monobase configuration). In other examples, a bibase configuration can be implemented, wherein 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 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 the first wireless device.

[0141] In operation 1004, process 1000 includes determining the presence of a user based on RF sensing data associated with a first received waveform. In some examples, the RF sensing data may include CSI data corresponding to reflections received in response to the transmission of the first RF waveform. In other examples, the RF sensing data may include data associated with at least one received leakage signal that is not reflected from any object and corresponds to the first RF waveform. The RF sensing data can be used to detect the presence of a user, and may include detecting user movement, detecting the presence of a user (e.g., head presence), detecting the direction of the user's head, or any combination thereof. In some examples, detecting the presence of a user may include tracking the user's movement and determining that the user is within a threshold distance to the wireless device.

[0142] 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 the reflected signal. In some examples, the distance can be determined based on the time of flight of the reflected signal, which is adjusted based on the propagation delay of the direct path (e.g., leakage signal between the transmitting and receiving antennas). In some examples, the angle of arrival can be based on the signal phase difference measured at each element of the receiver antenna array.

[0143] In some aspects, the first wireless device may transmit a second RF waveform with a higher bandwidth than the first RF waveform. The first wireless device may receive a second received waveform as a reflection of the second RF waveform from a user, and determine at least one of the presence of the user's head or the direction of the user's head based on RF sensing data associated with the second received waveform. In some examples, the second RF waveform may also include a different number of spatial links than the first RF waveform.

[0144] In some examples, the presence and orientation of the user's head can be used to initiate further activities on a first wireless device and / or other wireless devices. For example, determining that the user's head is absent for a predetermined period of time (e.g., the head presence is detected as false) may result in locking access to the wireless device. In another example, determining that the user's head orientation deviates for a predetermined period of time may result in the display backlight on the first wireless device dimming.

[0145] In 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 can be performed by configuring an RF interface on a first wireless device to transmit one or more extremely high frequency (EHF) waveforms (e.g., multiple EHF waveforms). The first wireless device may receive multiple reflected waveforms corresponding to one or more EHF waveforms. RF sensing data corresponding to the received reflections of 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 may include capturing an image of the user's face using an infrared camera.

[0146] In some aspects, the first wireless device may determine that a user is authorized to access the first wireless device based on facial authentication. For example, the first wireless device may calculate a similarity score (e.g., a similarity score of 807) and determine that the similarity score meets or exceeds a similarity threshold for granting access to the first wireless device. In some examples, in response to determining that a user is authorized to access the first wireless device (e.g., based on facial authentication), the first wireless device may enable access to the first wireless device.

[0147] In some cases, the first wireless device may determine that a user is authorized to access one or more other wireless devices based on facial authentication. In some aspects, in response to determining that a user is authorized to access one or more other wireless devices, the first wireless device may allow access to at least one wireless device from the one or more 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 from the one or more wireless devices may correspond to a vehicle that 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 from the one or more wireless devices may correspond to an Internet of Things (IoT) device that can be configured to provide access to a home (e.g., unlocking a door or opening a garage door).

[0148] Figure 11 is a flowchart illustrating an example of a process 1100 for performing head detection. In operation 1102, process 1100 includes receiving a first received waveform as a reflection of a first RF waveform via a wireless device. In some examples, the first RF waveform is transmitted by the same device (via the wireless device) that receives the first received waveform (e.g., a monobase configuration). In other examples, a bibase configuration can be implemented, wherein the first RF waveform may be transmitted by another wireless device (e.g., 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 the first wireless device.

[0149] In operation 1104, process 1100 includes determining at least one of the presence of a user's head or the orientation of a user's head based on RF sensing data associated with a first received waveform. In some examples, the RF sensing data may include CSI data corresponding to reflections received in response to the transmission of the first RF waveform. In other examples, the RF sensing data may include data associated with at least one received leakage signal that is not reflected from any object and corresponds to the first RF waveform. In some aspects, determining the presence of a user's head may include determining that the user's head is within a threshold distance of the wireless device. In some cases, determining the orientation of the user's head may include determining that the user is facing the wireless device for a predetermined time.

[0150] In some examples, the wireless device may implement one or more different RF sensing algorithms to determine or detect the presence and / or orientation of a user's head. In some cases, the RF sensing algorithms may have different levels of resolution and / or power consumption. In some cases, the resolution and / or power consumption of the RF sensing algorithms may be based on bandwidth, the number of spatial links, sampling rate, or any combination thereof. In some aspects, if the wireless device determines that the user's head is facing the device, the wireless device may initiate facial authentication. In further examples, the wireless device may determine that the user's head is facing a different device. The wireless device may use device location data, device orientation data, indoor map data, or any other suitable data to identify other devices near the user. In some cases, the wireless device may communicate with other devices based on the determination of the user's presence and / or head orientation. For example, if the wireless device determines that the user is facing and / or near a television, the wireless device may send a signal to enable the television to turn on.

[0151] 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 equipment 107 of FIG. 1. In another example, process 1000 may be performed by a computing device having the computing system 1200 shown in FIG. 12. For example, a computing device having the computing architecture shown in FIG. 12 may include components of user equipment 107 of FIG. 1 and may implement the operations of FIG. 10.

[0152] 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 the 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. 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.

[0153] Components of a computing device may be implemented in circuitry. For example, these components may include and / or may be implemented using electronic circuitry or other electronic hardware, which may include one or more programmable electronic circuits (e.g., a microprocessor, graphics processing unit (GPU), digital signal processor (DSP), central processing unit (CPU), and / or other suitable electronic circuitry), and / or may include and / or may be implemented using computer software, firmware, or any combination thereof to perform the various operations described herein.

[0154] Process 1000 is illustrated in the form of a logic flowchart, whose operations represent a series of operations that can be implemented in hardware, computer instructions, or combinations thereof. In the context of computer instructions, these operations represent computer-executable instructions stored on one or more computer-readable storage media, which, when executed by one or more processors, perform the operations. Typically, computer-executable instructions include routines, programs, objects, components, data structures, etc., that perform a specific function or implement a specific data type. The order in which the operations are described is not intended to be construed as limiting, and any number of described operations can be combined in any order and / or in parallel to implement the process.

[0155] Furthermore, the process 1000 and / or other processes described herein can be executed under the control of one or more computer systems configured with executable instructions, and can be hardware implemented as code (e.g., executable instructions, one or more computer programs, or one or more application programs), or a combination thereof, that executes collectively on one or more processors. As described above, the code can be stored, for example, on a computer-readable or machine-readable storage medium in the form of a computer program comprising multiple instructions executable by one or more processors. The computer-readable or machine-readable storage medium can be non-transitory.

[0156] Figure 12 is a diagram illustrating an example of a system for implementing certain aspects of the present technology. Specifically, Figure 12 illustrates an example of a computing system 1200, which can be, for example, any computing device constituting an internal computing system, a remote computing system, a camera, or any component of the system in which components communicate with each other using connection 1205. Connection 1205 can be a physical connection using a bus, or a direct connection to processor 1210, for example, in a chipset architecture. Connection 1205 can also be a virtual connection, a network connection, or a logical connection.

[0157] In some embodiments, the computing system 1200 is a distributed system, wherein the functions described herein may be distributed across data centers, multiple data centers, peer-to-peer networks, etc. In some embodiments, one or more of the system components represent a plurality of such components, each performing some or all of the functions of the described components. In some embodiments, the components may be physical or virtual devices.

[0158] Example system 1200 includes at least one processing unit (CPU or processor) 1210 and a connection 1205 that communicatively couples various system components, including system memory 1215 (e.g., read-only memory (ROM) 1220 and random access memory (RAM) 1225), to processor 1210. Computing system 1200 may include a cache 1212 of high-speed memory, which is directly connected to, close to, or integrated into processor 1210.

[0159] Processor 1210 may include any general-purpose processor and hardware or software services, such as services 1232, 1234, and 1236 stored in storage device 1230, which are configured to control processor 1210, as well as dedicated processors, where software instructions are incorporated into the actual processor design. Processor 1210 can essentially be a completely independent computing system, containing multiple cores or processors, buses, memory controllers, caches, etc. Multi-core processors can be symmetric or asymmetric.

[0160] To enable user interaction, the computing system 1200 includes an input device 1245, which can represent any number of input mechanisms, such as a microphone for voice, a touch screen for gesture or graphic input, a keyboard, a mouse, motion input, voice, etc. The computing system 1200 may also include an output device 1235, which can be one or more of various output mechanisms. In some instances, a multi-mode system allows the user to provide multiple types of input / output to communicate with the computing system 1200.

[0161] The computing system 1200 may include a communication interface 1240, which typically controls and manages user input and system output. The communication interface may use wired and / or wireless transceivers to perform or facilitate the reception and / or transmission of wired or wireless communications, including using audio jacks / plugs, microphone jacks / plugs, Universal Serial Bus (USB) ports / plugs, Apple™ Lightning™ ports / plugs, Ethernet ports / plugs, fiber optic ports / plugs, proprietary wired ports / plugs, 3G, 4G, 5G and / or other cellular data network wireless signal transmission, Bluetooth™ wireless signal transmission, Bluetooth™ Low Energy (BLE) wireless signal transmission, IBEACON™ wireless signal transmission, Radio Frequency Identification (RFID) wireless signal transmission, Near Field Communication (NFC) wireless signal transmission, Dedicated Short Range Communication (DSRC) wireless signal transmission, and 802.11. Wi-Fi wireless signal transmission, Wireless Local Area Network (WLAN) signal transmission, Visible Light Communication (VLC), Global Microwave Access Interoperability (WiMAX), Infrared (IR) wireless signal transmission, Public Switched Telephone Network (PSTN) signal transmission, Integrated Flow 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 combinations thereof.

[0162] The communication interface 1240 may also include one or more range sensors (e.g., light detection and ranging (LIDAR) sensors, laser rangefinders, radar, ultrasonic sensors, and infrared (IR) sensors) configured to collect data and provide measurements to the processor 1210, whereby the processor 1210 may be configured to perform determinations and calculations required to obtain various measurements from one or more range sensors. In some examples, measurements may include time of flight, wavelength, azimuth, elevation, distance, linear velocity and / or angular velocity, or any combination thereof. The communication interface 1240 may also include one or more Global Navigation Satellite System (GNSS) receivers or transceivers for determining the location of the computing system 1200 based on one or more signals received from one or more satellites associated with one or more GNSS systems. GNSS systems include, but are not limited to, the U.S. Global Positioning System (GPS), the Russian Global Navigation Satellite System (GLONASS), the Chinese BeiDou Navigation Satellite System (BDS), and the European Galileo GNSS. There are no restrictions on operation for any specific hardware configuration, so the basic functionality here can be easily replaced with improved hardware or firmware configurations during development.

[0163] Storage device 1230 may be a non-volatile and / or non-transitory and / or computer-readable storage device, or a hard disk or other type of computer-readable media capable of storing computer-accessible data, such as magnetic tape, flash memory card, solid-state storage device, digital multifunction disk, cassette memory, floppy disk, floppy disk, hard disk, magnetic tape, magnetic stripe / magnetic stripe card, any other magnetic storage media, flash memory, memristor memory, any other solid-state memory, optical disc read-only memory (CD-ROM), rewritable optical disc (CD), digital video disc (DVD), Blu-ray disc (BDD), holographic disc, another optical media, secure digital (SD) card, microsecure digital (microSD) card, Memory Stick® card, smart card chip, EMV chip, subscriber identification module (SIM) card, mini / micro / nano / Micro SIM card, another 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, cache (e.g., level 1 (L1) cache, level 2 (L2) cache, level 3 (L3) cache, level 4 (L4) cache, level 5 (L5) cache or other level (L#) cache, resistive random access memory (RRAM / ReRAM), phase change memory (PCM), spin-transfer torque RAM (STT-RAM), another storage chip or cassette memory and / or combinations thereof.

[0164] Storage device 1230 may include software services, servers, services, etc., which cause the system to perform functions when processor 1210 executes code defining such software. In some embodiments, hardware services performing a particular function may include software components stored in computer-readable media connected to desired hardware components (e.g., processor 1210, connection 1205, output device 1235, etc.) to perform that function. The term "computer-readable media" includes, but is not limited to, portable or non-portable storage devices, optical storage devices, and various other media capable of storing, containing, or carrying instructions and / or data. Computer-readable media may include non-transitory media capable of storing data, excluding carrier waves and / or transient electronic signals propagated wirelessly or via wired connections. Examples of non-transitory media may include, but are not limited to, magnetic disks or magnetic tapes, optical storage media such as optical discs (CDs) or digital multifunction discs (DVDs), flash memory, memory, or memory devices. Computer-readable media may store code and / or machine-executable instructions thereon, which may represent any combination of procedures, functions, subroutines, programs, routines, subroutines, modules, software packages, classes, or instructions, data structures, or program statements. A code segment may be coupled to another code segment or hardware circuitry by passing and / or receiving information, data, parameters, or memory contents. Information, parameters, data, etc., may be passed, forwarded, or transmitted by any appropriate means, including memory sharing, message passing, token passing, network transmission, etc.

[0165] Specific details have been provided in the foregoing description to provide a thorough understanding of the embodiments and examples provided herein, but those skilled in the art will recognize that this application is not limited thereto. Therefore, while exemplary embodiments of this application have been described in detail herein, it should be understood that the concepts of the invention can be embodied and applied in various other ways and in various manners, and the appended claims are intended to be construed as including such variations unless limited by the prior art. Various features and aspects of the above applications can be used alone or in combination. Furthermore, embodiments can be used in any number of environments and applications beyond those described herein without departing from the broader spirit and scope of this specification. Therefore, the specification and drawings should be considered illustrative rather than restrictive. For ease of illustration, various methods have been described in a particular order. It should be understood that in alternative embodiments, these methods may be performed in a different order than described.

[0166] For clarity, in some cases, the technology may be presented as comprising single functional blocks, which include devices, device components, steps or routines in methods contained in software, or combinations 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, and other components may be shown as components in block diagram form to avoid obscuring the embodiments with unnecessary detail. In other cases, to avoid obscuring the embodiments, known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail.

[0167] Furthermore, those skilled in the art will understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in connection with the aspects disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, various illustrative components, blocks, modules, circuits, and steps have been generally described above in terms of their functionality. Whether this functionality is implemented as hardware or software depends on the specific application and design constraints imposed on the entire system. Those skilled in the art can implement the described functionality in different ways for each specific application, but such implementation decisions should not be construed as causing a departure from the scope of this disclosure.

[0168] The above embodiments can be described as processes or methods shown in flowcharts, flow diagrams, data flow diagrams, structure diagrams, or block diagrams. Although a flowchart can describe operations as a continuous process, many operations can be executed in parallel or simultaneously. Furthermore, the order of operations can be rearranged. A process terminates when its operations are completed, but there may be additional steps not included in the diagram. A process may correspond to a method, function, procedure, subroutine, subroutine, etc. When a process corresponds to a function, its termination may correspond to the function returning to the calling function or the main function.

[0169] The processes and methods according to the above examples can be implemented using computer-executable instructions stored in or otherwise obtainable from a computer-readable medium. For example, such instructions may include instructions and data that cause or otherwise configure a general-purpose computer, special-purpose computer, or processing device to perform a particular function or set of functions. Some of the computer resources used may be accessible via a network. For example, the computer-executable instructions may be binary files, such as assembly language, firmware, or intermediate format instructions of source code. Examples of computer-readable media that can be used to store instructions, information used, and / or information created during the methods according to the examples include magnetic disks or optical disks, flash memory, USB devices with non-volatile memory, network storage devices, etc.

[0170] In some embodiments, computer-readable storage devices, media, and memories may include cable or wireless signals containing bit streams, etc. However, when referred to as non-transitory computer-readable storage media, media such as energy, carrier signals, electromagnetic waves, and the signals themselves are explicitly excluded.

[0171] Those skilled in the art will understand that information and signals can be represented using any of a variety of different techniques and skills. For example, data, instructions, commands, information, signals, bits, symbols and chips that may be referenced in the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles or any combination thereof, depending in some cases on the specific application, the required design, the appropriate technology, etc.

[0172] The various illustrative logic blocks, modules, and circuits described in connection with the aspects disclosed herein can be implemented or executed using hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof, and can take any of a variety of form factors. When implemented in software, firmware, middleware, or microcode, program code or code segments for performing necessary tasks (e.g., computer program products) can be stored in computer-readable or machine-readable media. The processor can perform the necessary tasks. Examples of form factors include laptops, smartphones, mobile phones, tablet devices or other small personal computers, personal digital assistants, rack-mount devices, standalone devices, etc. The functionality described herein can also be embodied in peripheral devices or plug-in cards. As a further example, this functionality can also be implemented on circuit boards between different chips or in different processes executed in a single device.

[0173] Instructions, media for transmitting such instructions, computing resources for executing them, and other structures for supporting such computing resources are example means of providing the functionality described in this disclosure.

[0174] The techniques described herein can also be implemented in electronic hardware, computer software, firmware, or any combination thereof. Such techniques can be implemented in any of a variety of devices, such as general-purpose computers, wireless communication handheld devices, or multi-purpose integrated circuit devices, including applications in wireless communication handheld devices and other devices. Any feature described as a module or component can be implemented together in an integrated logic device or separately as a discrete but interoperable logic device. If implemented in software, the techniques can be implemented at least in part by a computer-readable storage medium comprising program code that, when executed, performs one or more of the methods, algorithms, and / or operations described above. The computer-readable storage medium can form part of a computer program product, which may include packaging materials. Computer-readable media may include memory or data storage media, such as random access memory (RAM), such as synchronous dynamic random access memory (SDRAM), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), flash memory, magnetic or optical data storage media, and the like. Additionally or alternatively, these technologies may be implemented at least in part through computer-readable communication media that carry or communicate program code in the form of instructions or data structures, and can be accessed, read, and / or executed by a computer, such as propagated signals or waves.

[0175] The program code can 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 circuits. Such a processor can be configured to perform any of the techniques described in this disclosure. A general-purpose processor may be a microprocessor; however, alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, a combination of one or more microprocessors with a DSP core, or any other such configuration. Therefore, the term "processor" as used herein may refer to any of the foregoing structures, any combination of the foregoing structures, or any other structure or means suitable for implementing the techniques described herein.

[0176] Those skilled in the art will understand that the less than (“<”) and greater than (“>”) symbols or terms used herein may be replaced with less than or equal to (“≤”) and greater than or equal to (“≥”) symbols without departing from the scope of this description.

[0177] When a component is described as being “configured” to perform certain operations, such operations may be performed by designing electronic circuits or other hardware, by programming programmable electronic circuits (e.g., microprocessors or other suitable electronic circuits), or by any combination thereof.

[0178] The phrase “coupled to” or “communicationally coupled to” means any component that is directly or indirectly physically connected to another component, and / or any component that communicates directly or indirectly with another component (e.g., connected to another component via a wired or wireless connection and / or other suitable communication interface).

[0179] The phrase "at least one of a set" and / or "one or more of a set" in the request term language or other languages ​​means that one or more members of the set (in any combination) satisfy the request term. For example, "at least one of A and B" or "at least one of A or B" in the request term language means A, B, or A and B. In another example, "at least one of A, B, and C" or "at least one of A, B, or C" in the request term language means A, B, C, or A and B, or A and C, or B and C, or A, B, and C. "At least one of a set" and / or "one or more of a set" in the language does not limit the items listed in the set. For example, "at least one of A and B" or "at least one of A or B" in the request term language can mean A, B, or A and B, and may also include items not listed in the set A and B.

[0180] The illustrative aspects of this disclosure include:

[0181] 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 a first received waveform as a reflection of a first radio frequency (RF) waveform via the at least one transceiver; determine the 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.

[0182] Aspect 2: The first wireless device according to aspect 1, wherein the at least one processor is configured to: transmit a second RF waveform having a higher bandwidth than the first RF waveform via the at least one transceiver; receive a second received waveform via the at least one transceiver, the second received waveform being a reflection of the second RF waveform from the user; and determine at least one of the presence of the user's head or the direction of the user's head based on RF sensing data associated with the second received waveform.

[0183] Aspect 3: A first wireless device according to any one of Aspect 1 or 2, wherein the at least one processor is configured to: dim the display backlight on the first wireless device in response to determining that the user’s head orientation has deviated within a predetermined time.

[0184] Aspect 4: A first wireless device according to 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 within a predetermined time.

[0185] Aspect 5: A first wireless device according to 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.

[0186] Aspect 6: The first wireless device according to any one of aspects 1 to 5, wherein the first RF waveform is transmitted by the second wireless device.

[0187] Aspect 7: The first wireless device according to any one of aspects 1 to 6, wherein the RF sensing data includes channel state information (CSI) data.

[0188] Aspect 8: A first wireless device according to any one of aspects 1 to 7, wherein, in order to determine the presence of the user, at least one processor is configured to: track the actions of the user; and determine that the user is within a threshold distance to the first wireless device.

[0189] Aspect 9: A first wireless device according to any one of aspects 1 to 8, wherein, in order to initiate facial authentication of the user, the at least one processor is configured to: transmit a plurality of extremely high frequency (EHF) waveforms via the at least one transceiver; 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.

[0190] Aspect 10: The first wireless device according to any one of aspects 1 to 9 further includes a camera, wherein, in order to initiate facial authentication of the user, at least one processor is configured to: capture at least one image of the user's face using the camera.

[0191] Aspect 11: A first wireless device according to any one of aspects 1 to 10, wherein the RF sensing data includes data associated with at least one received leakage signal, the leakage signal not reflected from any object and corresponding to the first RF waveform.

[0192] Aspect 12: A first wireless device according to any one of aspects 1 to 11, wherein at least one processor is configured to: determine, based on the facial authentication, that the user is authorized to access the first wireless device; and, in response to determining that the user is authorized to access the first wireless device, enable access to the first wireless device.

[0193] Aspect 13: A first wireless device according to any one of aspects 1 to 12, wherein the at least one processor is configured to: determine, based on the facial authentication, that the user is authorized to access 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 at least one wireless device from the one or more other wireless devices.

[0194] Aspect 14: A method for performing facial recognition, the method comprising operations according to any one of aspects 1 to 13.

[0195] 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.

[0196] Aspect 16: An apparatus for facial recognition, the apparatus comprising components for performing operations according to any one of aspects 1 to 13.

[0197] Aspect 17: A wireless device for determining the presence of a user. 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 a first received waveform as a reflection of a first radio frequency (RF) waveform; determine the 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 a second RF waveform having a higher bandwidth than the first RF waveform via the at least one transceiver; process the second received waveform as a reflection of the second RF waveform from the user; and determine at least one of the presence of the user's head or the direction of the user's head based on RF sensing data associated with the second received waveform.

[0198] Aspect 18: The wireless device according to aspect 17, wherein the at least one processor is configured to: in response to determining that the user’s head orientation has deviated within a predetermined time, dim the display backlight on the first wireless device.

[0199] Aspect 19: A wireless device according to any one of Aspects 17 or 18, wherein the 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 within a predetermined time.

[0200] Aspect 20: A wireless device according to 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.

[0201] Aspect 21: The wireless device according to any one of aspects 17 to 20, wherein the RF sensing data includes channel state information (CSI) data.

[0202] Aspect 22: A first wireless device according to any one of aspects 17 to 21, wherein, in order to determine the presence of the user, the at least one processor is configured to: track the movement of the user; and determine that the user is within a threshold distance to the first wireless device.

[0203] Aspect 23: The wireless device according to any one of aspects 17 to 22, wherein the RF sensing data includes data associated with at least one received leakage signal, the leakage signal not reflected from any object and corresponding to the first RF waveform.

[0204] Aspect 24: A method for determining the existence of a user, the method comprising operations according to any one of aspects 17 to 23.

[0205] 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.

[0206] Aspect 26: An apparatus for determining the presence of a user, the apparatus comprising components for performing operations according to any one of aspects 17 to 23. [Simplified Explanation of the Diagram]

[0015] The accompanying drawings are provided to help describe various aspects of this disclosure. The drawings are for illustrative purposes only and are not intended to limit these aspects.

[0016] Figure 1 is a block diagram illustrating an example of a computing system based on some examples of user equipment;

[0017] Figure 2 is a diagram illustrating examples of wireless devices that utilize radio frequency (RF) sensing technology to detect the presence of a user and perform facial recognition, according to some examples;

[0018] Figure 3 is a diagram illustrating an example of an environment including a wireless device for detecting the presence of a user and performing facial recognition, according to some examples;

[0019] Figure 4 is a flowchart illustrating an example of a process for performing facial recognition, based on some examples;

[0020] Figure 5 is a flowchart illustrating an example of a process for performing facial recognition, based on some examples;

[0021] Figure 6 is a flowchart illustrating another example of a process for performing face recognition, based on some examples;

[0022] Figure 7 is a flowchart illustrating another example of a process for performing facial recognition, based on some examples;

[0023] Figure 8 is a flowchart illustrating another example of a process for performing facial recognition, based on some examples;

[0024] Figure 9 is a flowchart illustrating an example of a process for performing device management based on user attention, according to some examples;

[0025] Figure 10 is a flowchart illustrating another example of a process for performing face recognition, based on some examples;

[0026] Figure 11 is a flowchart illustrating an example of a process for performing head detection according to some examples; and

[0027] Figure 12 is a block diagram illustrating an example of a computing system based on some examples.

Claims

1. A first wireless device for facial recognition, comprising: At least one transceiver; At least one memory cell; and at least one processor coupled to the at least one memory and the at least one transceiver, the at least one processor being configured to: receive a first received waveform as a reflection of a first radio frequency (RF) waveform via the at least one transceiver; determine the presence of a user based on RF sensing data associated with the first received waveform, wherein the RF sensing data includes channel state information (CSI) data corresponding to the direct path of the first RF waveform and CSI data corresponding to the reflection path of the first received waveform; and initiate facial authentication of the user in response to determining the presence of the user.

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

3. The first wireless device according to claim 2, wherein the at least one processor is configured to: dim the display backlight on the first wireless device in response to determining that the user's head orientation has deviated within a predetermined time.

4. The first wireless device according to claim 2, wherein the at least one processor is configured to: lock access to the first wireless device in response to determining within a predetermined time that the presence of the user's head is false.

5. The first wireless device according to claim 1, wherein the first RF waveform includes a Wi-Fi signal transmitted by an omnidirectional antenna on the first wireless device.

6. The first wireless device according to claim 1, wherein the first RF waveform is transmitted by the second wireless device.

7. The first wireless device according to claim 1, wherein, To determine the presence of the user, the at least one processor is configured to: track the movement of the user; and determine that the user is within a threshold distance to the first wireless device.

8. The first wireless device according to claim 1, wherein, To initiate facial authentication for the user, the at least one processor is configured to: transmit a plurality of extremely high frequency (EHF) waveforms via the at least one transceiver; 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.

9. The first wireless device according to claim 1, further comprising a camera, wherein, In order to initiate facial authentication for the user, the at least one processor is configured to: capture at least one image of the user's face using the camera.

10. The first wireless device according to claim 1, wherein the RF sensing data further includes CSI data associated with at least one received leakage signal, the leakage signal not reflected from any object and corresponding to the first RF waveform and the direct path.

11. The first wireless device according to claim 1, wherein the at least one processor is configured to: determine, based on the facial authentication, that the user is authorized to access the first wireless device; and, in response to determining that the user is authorized to access the first wireless device, enable access to the first wireless device.

12. The first wireless device according to claim 1, wherein the at least one processor is configured to: determine, based on the facial authentication, that the user is authorized to access 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 at least one wireless device from the one or more other wireless devices.

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