Biometric authentication by vascular monitoring

Abstract

Presented here are methods for authenticating unknown individuals based on changes in the properties of blood vessels over time. At a high level, these methods rely on monitoring changes in the properties of blood vessels over time. Examples of properties include the position, size, volume, and pressure of blood vessels, as well as the speed and acceleration of blood flowing through blood vessels.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority to U.S. Provisional Application No. 63 / 115,941, filed November 19, 2020, entitled “Arterial Pocket Authentication,” the entirety of which is incorporated herein by reference. Technical Field

[0003] This disclosure relates to biometric authentication in computer security, and more specifically, to techniques for examining physiological characteristics to achieve minimally destructive authentication. Background Technology

[0004] Biometric authentication procedures verify an individual's identity using biometric technology. The term "biometric" refers to physical or behavioral characteristics that can be used as a means of verifying identity. Biometric technology is difficult to fake and convenient because the individual does not need to remember a password or manage a token. Instead, the authentication mechanism is an integral part of the individual.

[0005] Historically, fingerprints have been the most common form of biometrics. However, with technological advancements, other biometric modalities have emerged. For example, vascular pattern recognition (also known as "vein pattern authentication") uses near-infrared light to create images of subcutaneous blood vessels. These subcutaneous vessels collectively define the "vascular pattern" that can be used for authentication. Vascular pattern authentication has shown promise because the vascular pattern is not only unique to the individual but also undergoes only minimal changes as the individual ages.

[0006] Vein pattern authentication typically involves identifying and subsequently analyzing the vascular pattern on the back of the hand. For example, near-infrared light generated by a light-emitting diode (LED) is emitted towards the back of the hand, allowing it to penetrate the skin. Due to the difference in absorption rates between blood vessels and other tissues, the near-infrared light is reflected to different depths of the skin. Based on the analysis of the reflected near-infrared light, the vascular pattern can be inferred, and from the pattern, features such as branch locations and angles can be determined (and then used for authentication).

[0007] Vein pattern authentication is touted as a contactless option for biometric authentication, offering relative immunity to forgery because vein patterns are difficult to reproduce. However, vein pattern authentication has several drawbacks. While individuals may not need to touch the sensing surface as in fingerprint authentication, they still must present the back of their hand for analysis. This can be difficult in some situations (e.g., when the individual is not near the payment system) and cumbersome in others (e.g., when the individual is wearing gloves). Attached Figure Description

[0008] Figure 1 This includes advanced illustrations of traditional authentication procedures, in which unknown individuals are prompted to show their hands to a blood vessel scanner.

[0009] Figure 2A This includes a high-level representation of the system, which can be used to authenticate the identity of unknown individuals whose blood vessels are available for scanning.

[0010] Figure 2B This includes a high-level representation of the system, which can be used to authenticate the identity of unknown individuals based on gesture recognition.

[0011] Figure 2C This includes a high-level representation of the system, which can be used to authenticate the identity of unknown individuals based on location identification.

[0012] Figure 3 The illustration shows an example of an electronic device capable of implementing an authentication platform designed to authenticate the identity of unknown persons based on data generated by signal sensors.

[0013] Figures 4A-4B The document includes a flowchart of a procedure for authenticating users of an authentication platform based on radar detection of vascular dynamics, which are monitored using electronic devices located near the body.

[0014] Figures 5A-5B The flowcharts include the registration and usage phases in an embodiment where an authentication platform determines whether to authenticate a user based on gesture recognition.

[0015] Figures 6A-6B The flowcharts include the registration and usage phases in an embodiment where an authentication platform determines whether to authenticate a user based on location identification.

[0016] Figures 7A-7B This includes a flowchart of the registration and usage phases, representing an example of a certification process based on vascular dynamics.

[0017] Figure 8 The illustration includes a scenario where authentication of an unknown person still occurs even though the person leaves their electronic device in their front trouser pocket.

[0018] Figure 9 The illustration shows an example of a schematic implementation of an authentication platform that can authenticate unknown individuals based on the vascular dynamics of blood vessels.

[0019] Figure 10 A flowchart depicts the process for biometric authentication of the identity of a person claiming to be a given individual.

[0020] Figure 11 A flowchart depicts the process for generating a biometric signature for an individual, which can then be used for authentication.

[0021] Figure 12 A flowchart is depicted for the process of determining whether a person should be identified as a given individual based on the vascular dynamics of blood vessels changing over time.

[0022] Figure 13 The block diagram illustrates an example of a processing system in which at least some of the operations described herein can be implemented.

[0023] For those skilled in the art, the various features of the technology described herein will become clearer upon examination of the detailed description in conjunction with the accompanying drawings. Embodiments are illustrated in the drawings by way of example and not limitation. While various embodiments are depicted for illustrative purposes, those skilled in the art will recognize that alternative embodiments may be employed without departing from the principles of the technology. Therefore, although specific embodiments are shown in the drawings, various modifications can be made to the technology. Detailed Implementation

[0024] To join an authentication process that relies on vein matching, an individual (also referred to as a “user”) may initially be prompted to present their hand to a vascular scanner. The term “vascular scanner” can be used to refer to an imaging instrument that includes (i) an emitter operable to emit electromagnetic radiation (e.g., in the near-infrared range) toward the body, and (ii) a sensor operable to sense electromagnetic radiation reflected by physiological structures within the body. Typically, an image is created based on the reflected electromagnetic radiation as a reference template. At a high level, the reference template represents a “ground truth” pattern of blood vessels that can be used for authentication.

[0025] Figure 1 This includes high-level illustrations of traditional authentication procedures, where unknown individuals are prompted to present their hands to a blood vessel scanner. For example... Figure 1As shown, a vascular scanner emits electromagnetic radiation into the hand and then creates an image (also known as a "scan") based on the electromagnetic radiation reflected by the blood vessels in the hand. This image indicates the pattern of blood vessels in the hand, which can then be verified against a reference template created for a given individual during the registration phase (also known as the "registration phase"). If the image matches the reference template, the unknown person will be authenticated as the given individual. However, if the image does not match the reference template, the unknown person will not be authenticated as the given individual.

[0026] Because vascular scanners do not require contact with the body part being scanned, vein matching has become an attractive option for biometric authentication. However, vein matching has proven susceptible to spoofing. As an example, Jan Krissler and Julian Albrecht demonstrated at the 2018 Chaos Communications Conference how a wax prosthetic hand could be used to bypass a vascular scanner. While spoofing is unlikely to succeed under most real-world conditions, any concerns associated with vulnerabilities could hinder the adoption of reliable biometric authentication technologies.

[0027] Therefore, this section introduces methods for identifying unknown individuals based on changes in vascular properties over time. At a high level, these methods rely on monitoring vascular dynamics to identify unknown individuals. The term "vascular dynamics" refers to changes in vascular properties that occur over time. Examples of properties include the location, size, volume, and pressure of blood vessels, as well as the velocity and acceleration of blood flowing through them.

[0028] To prevent deception, an authentication platform (also known as an "authentication system") can determine the similarity between the vascular dynamics of an unknown person whose identity is to be authenticated and those of a given individual. For example, suppose an unknown person wishes to authenticate themselves as a given individual. In this scenario, the unknown person might be prompted to position a body part near a vascular scanner. The vascular scanner can then emit a series of signals into the body part, generating data representing the signals reflected by physiological structures (including blood vessels) located within the body part. By analyzing this data, the authentication platform can determine, derive, or otherwise obtain a metric indicating the unknown person's vascular dynamics. This metric may be referred to as the unknown person's "biometric signature" or "vascular signature." The authentication platform can then compare this biometric signature with at least one registered biometric signature associated with a given individual to determine whether the unknown person should be authenticated as that individual. Thus, the authentication platform can establish the probability that the unknown person is the given individual based on a comparison of their vascular dynamics.

[0029] Authentication based on vascular dynamics offers many of the same benefits as vein matching, namely high accuracy, reliability, and consistency, because the information being "read" is inside the body. However, vascular dynamics are less easily faked because variations in vascular patterns and properties can be used as unique biometric techniques.

[0030] For illustrative purposes, embodiments can be described in the context of monitoring the vascular dynamics of the femoral artery. However, the methods described herein can be similarly applied to any artery, arteriole, capillary, venule, or vein. Similarly, the methods described herein can be similarly applied to any group of vessels. Therefore, in situations seeking authentication for sensitive tasks, the authentication platform can monitor the vascular dynamics of more than one vessel.

[0031] While not strictly necessary, the following description of implementation is within the context of instructions executable by electronic devices. The term "electronic device" is generally used interchangeably with the term "computing device," and can thus refer to computer servers, POS systems, tablet computers, wearable devices (e.g., fitness trackers and watches), mobile phones, and so on.

[0032] While some aspects of this technology, such as certain modules, can be described as being performed entirely or primarily by a single electronic device, some implementations are carried out in a distributed environment where modules are shared among multiple electronic devices linked via a network. For example, an unknown person might be required to initiate an authentication process by a POS system, but the measurement of vascular dynamics might be performed by a mobile phone located near that unknown person. Similarly, while a mobile phone might be responsible for measuring the vascular dynamics of an unknown person, the decision to authenticate that person might be made by an authentication platform residing on a computer server, with the mobile phone communicating with that server.

[0033] the term

[0034] In this disclosure, the reference to "an embodiment" or "some embodiments" means that the described features, functions, structures, or characteristics are included in at least one embodiment. The appearance of such phrases does not necessarily refer to the same embodiment, nor does it necessarily refer to mutually exclusive alternative embodiments.

[0035] Unless the context explicitly requires otherwise, the terms “including,” “comprising,” and “consisting of” should be interpreted in an inclusive sense, not an exclusive sense (i.e., in the sense of “including but not limited to”). The term “based on” should also be interpreted in an inclusive sense, not an exclusive sense. Thus, unless otherwise noted, the term “based on” means “at least partially based on.”

[0036] The terms “connection,” “coupling,” and their variations are intended to encompass any connection or coupling between two or more elements, whether direct or indirect. Connections / couplings can be physical, logical, or a combination thereof. For example, elements may be electrically or communicatively coupled to each other without sharing a physical connection.

[0037] The term "module" can refer broadly to software, firmware, and / or hardware. A module is typically a functional component that generates one or more outputs based on one or more inputs. A computer program may include or utilize one or more modules. Thus, a computer program may utilize multiple modules responsible for performing different tasks or a single module responsible for performing all tasks.

[0038] When used to refer to a list of multiple items, the term "or" is intended to cover all interpretations of: any item in the list, all items in the list, and any combination of items in the list. The term "and / or" should be interpreted in a similar manner. For example, suppose an embodiment is described as including "a first item, a second item, and / or a third item." In this case, the phrase is intended to cover a single first item, a single second item, a single third item, a combination of the first and second items, a combination of the first and third items, and a combination of the second and third items.

[0039] The order of steps performed in any process described herein is exemplary. However, these steps may be performed in various orders and combinations, unless contrary to physical possibility. For example, steps may be added to or removed from the processes described herein. Similarly, steps may be substituted or reordered. Thus, the description of any process is intended to be open-ended.

[0040] Authentication based on analysis of vascular information

[0041] This section introduces the use of vascular dynamics as a platform for verifying biometric evidence that an unknown person is a given individual. As discussed further below, vascular dynamics of an unknown person can be measured using probe signals emitted into the body. The reflections of these probe signals can be detected by sensors positioned near the body. These reflections can represent “return signals,” from which changes in vascular properties can be determined.

[0042] Authentication platforms can be used to secure biometric-driven transactions, such as payments authorized via hands-free interfaces. For example, suppose an unknown person wishes to authenticate themselves to complete a transaction. Instead of prompting the unknown person to position a body part (e.g., their hand) near a vascular scanner, biometric authentication is performed using an electronic device already located near their body. This device could be located in a pocket of clothing worn on the body. As an example, a mobile phone in the pocket of an unknown person's trousers could include a transmitter operable to emit a probe signal, and sensors operable to sense the probe signal reflected by physiological structures within the body. While this authentication method relies on the analysis of blood vessels beneath the skin, the mobile phone does not need to touch the skin, nor does it need to "see" the skin. Therefore, authentication platforms can allow people to authenticate themselves in a minimally invasive manner by relying on information about vascular dynamics.

[0043] The following section discusses several different authentication methods in more detail. These methods include:

[0044] • Vessel pattern authentication: Authentication is based on the pattern of blood vessels determined from the returned signal;

[0045] • Vascular dynamic authentication: Authentication is based on measured vascular characteristics; and

[0046] • Vascular Deformation Authentication: Authentication is based on vascular characteristics measured when an unknown person interacts with the environment, such as by performing gestures that cause vascular deformation.

[0047] In some embodiments, the authentication platform operates independently to authenticate the identity of an unknown person, while in other embodiments, it operates in conjunction with another system. For example, a payment system may interface with the authentication platform to ensure transactions are completed securely and without hassle. As an example, the authentication platform may facilitate contactless payment procedures in which an unknown person is allowed to initiate or complete a transaction by making a body part available for scanning. As described above, an unknown person can make a body part available for scanning simply by positioning a vascular scanner near her body (e.g., in the pocket of the unknown person's trousers).

[0048] Note that while the embodiments may discuss authentication within the context of initiating or completing a transaction, authentication can function in a variety of contexts. For example, suppose a group of individuals are invited to attend a network-accessible meeting where sensitive information will be shared. Each person attempting to enter the network-accessible meeting may need to be authenticated by an authentication platform before being allowed access.

[0049] Overview of the authentication platform

[0050] Figure 2AThis includes a high-level representation of System 200A, which can be used to authenticate the identity of unknown individuals whose blood vessels are available for scanning. For example... Figure 2A As shown, system 200A includes an authentication platform 202A that has access to a user interface (UI) 204, a signal transmitter 206, a signal sensor 208, and a processor 210. As further discussed below, these elements of system 200A can be embedded in the same electronic device or distributed among multiple electronic devices. For example, the authentication platform 202A can reside partially or entirely on a network-accessible server system, while the UI 204, signal transmitter 208, signal sensor 208, and processor 210 can reside on another electronic device located near an unknown person.

[0051] UI 204 represents the interface through which an unknown person can interact with system 200. UI 204 can be a voice-driven graphical user interface (GUI) displayed on the display of an electronic device. Alternatively, UI 204 can be a non-voice-driven GUI displayed on the display of an electronic device. In such embodiments, UI 204 can visually indicate body parts presented for authentication purposes. For example, UI 204 can visually prompt an unknown person to present their hand to signal transmitter 206 and signal sensor 208 for scanning by presenting its image.

[0052] Signal transmitter 206 (also referred to as a “signal generator”) can be configured to emit electromagnetic radiation in the form of pulses toward a body part presented by an unknown person. For example, suppose UI 204 instructs an unknown person to present her hand for authentication. In this scenario, signal transmitter 206 can emit signals toward the hand at time intervals. Typically, this time interval is between 0.1 and 1.0 seconds, although it can be longer or shorter in some embodiments. Meanwhile, signal sensor 208 can be any sensing device capable of generating data representing signals reflected by physiological structures located in the body. At a higher level, signal transmitter 206 can be responsible for emitting signals toward the body at time intervals, while signal sensor 208 can be responsible for collecting the reflected signals. For convenience, the signal emitted into the body can be referred to as a “detection signal” or a “measurement signal,” and the reflected signal can be referred to as a “return signal.”

[0053] Those skilled in the art will recognize that reflection occurs on the surface where the signal is incident. The reflecting surface is typically the boundary between two structures (e.g., blood vessels and tissues) with different electromagnetic properties. Ideally, the sensing device should be able to detect the returned signal with sufficient resolution to distinguish small differences (e.g., at the micrometer scale) in order to detect changes in vascular properties, as discussed further below. As an example, for large arteries with a wall diameter of 0.5–1.0 mm, a resolution at the tens of micrometer scale is often sufficient to detect changes caused by variations in blood flow. Smaller vessels often require higher resolution, e.g., at the micrometer or smaller scale.

[0054] In some embodiments, the signal transmitter 206 and the signal sensor 208 are part of an active sensing system. The active sensing system can be, for example, an integrated circuit (IC) designed to emit and sense electromagnetic radiation within a predetermined frequency range (e.g., 6 to 8 GHz, 6 to 8.5 GHz, 6 to 9 GHz). An IC capable of emitting and sensing electromagnetic radiation over a wide frequency range (e.g., greater than 2 GHz) can be referred to as an "ultra-wideband (UWB) IC".

[0055] It is worth noting that the signal transmitter 206 and the signal sensor 208 can operate together to capture information about blood vessels (more specifically, the properties of certain blood vessels) at a certain distance through obstacles. Therefore, an unknown person to be authenticated may not need to actually touch the signal transmitter 206 or the signal sensor 208, as discussed further below.

[0056] As described above, the signal transmitter 206 and signal sensor 208 can be embedded in a single electronic device. In some embodiments, the electronic device is associated with an unknown person. For example, the signal transmitter 206 and signal sensor 208 can be embedded in a mobile phone associated with an unknown person. In other embodiments, the electronic device is not associated with an unknown person. For example, the signal transmitter 206 and signal sensor 208 can be embedded in a POS system through which an unknown person is attempting to complete a transaction.

[0057] like Figure 2AAs shown, the authentication platform 202A may include a vascular dynamics (VD) signal characteristic algorithm 212, a return signal evaluation algorithm 214, a VD signature processing algorithm 216, an authentication algorithm 218, and a biometric database 220. The biometric database 220 can store biometric data representing collected information related to vascular attributes, which can be used to identify known individuals. As further discussed below, the biometric data in the biometric database 220 may vary depending on the authentication method employed by system 200A. The biometric data in the biometric database 220 may be encrypted, hashed, or otherwise obfuscated to prevent unauthorized access.

[0058] For example, in an embodiment of the authentication platform 200A that determines the appropriateness of authentication based on vascular dynamics, the biometric database 220 may include reference values ​​for different vascular attributes. Thus, the biometric database 220 may include data indicating the temporal variation of vascular attributes for a given vascular vessel. As further discussed below, this data can be used for authentication because the temporal variation can not only be uniquely associated with an individual, but also can vary across different vascular vessels of that individual.

[0059] As another example, the biometric database 220 may include models that indicate the deformation of a given blood vessel when a given individual or different individuals perform a gesture. Each model may represent a series of discrete locations indicating how the shape of a given blood vessel changes over time when the gesture is performed. These models may be stored in profiles associated with different individuals. A profile may include a single model associated with a single gesture, multiple models associated with a single gesture, or multiple models associated with different gestures.

[0060] Therefore, the biometric database 220 may include one or more biometric signatures. Each biometric signature may represent one or more values ​​that indicate the temporal variation of vascular properties of a given blood vessel in a given individual. As an example, a biometric signature may include a vector of length N, where each element is an intensity value based on the magnitude of the frequency offset between a probe signal and a corresponding return signal, thereby indicating the amplitude of vibration of the blood vessel wall. N may represent the number of samples acquired over time. In other words, N may represent the number of probe signals emitted into the body during the authentication process.

[0061] Biometric signatures in biometric database 220 can be associated with a single individual, in which case authentication platform 202A may be limited to authenticating an unknown person as that individual. Alternatively, these biometric signatures can be associated with multiple individuals, in which case authentication platform 202A may be able to authenticate an unknown person as any of these individuals. Thus, a single individual can have multiple biometric signatures in biometric database 220. These biometric signatures can correspond to different blood vessels and / or different electronic devices used for acquisition. For example, a single electronic device (e.g., a mobile phone) may be located near multiple anatomical regions (e.g., in different pockets), and may have a different biometric signature for each anatomical region. As another example, an individual may be associated with multiple electronic devices (e.g., mobile phones and wearable devices), and may have a different biometric signature for each electronic device.

[0062] In some embodiments, each biometric signature is assigned a quality rating that indicates how reliable the biometric signature is for authentication. The quality rating can be based on factors such as the signal-to-noise ratio (SNR) of the original returned signal, the repeatability of the original measurement, and so on. In embodiments where multiple biometric signatures are used for authentication, the corresponding quality ratings can be used to establish appropriate weights for these biometric signatures. At higher levels, more weight can be assigned to biometric signatures with higher quality ratings.

[0063] When executed by processor 210, the algorithm implemented in authentication platform 202A allows individuals to generate biometric signatures during the registration phase. Subsequently, the algorithm implemented in authentication platform 202A allows authentication to occur during the usage phase. References will be made below. Figures 4A-7B Further description of the registration and usage phases.

[0064] The VD signal characterization algorithm 212 can be responsible for defining the properties of the probe signal to be emitted into the body of the unknown person to be authenticated. For example, the VD signal characterization algorithm 212 can define characteristics such as: the frequency of the probe signal (e.g., to control penetration depth), amplitude (e.g., to return a stronger signal, but with a higher risk of noise), samples per second, timing of emission, duration of emission, or any combination thereof. The VD signal characterization algorithm 212 can take any of the following as input:

[0065] • Attributes of electronic devices, such as known location or power availability;

[0066] • Attributes of the signal transmitter 206, such as power requirements, maximum transmission amplitude, or maximum transmission frequency;

[0067] • The attributes of the signal sensor 208, such as power requirements, sensitivity, or frequency range; or

[0068] The output generated by the return signal evaluation algorithm 214 indicates, for example, a problem with data acquisition.

[0069] The return signal evaluation algorithm 214 can be responsible for evaluating the return signals by examining the data generated by the signal sensor 208. For example, the return signal evaluation algorithm 214 can evaluate each return signal to generate and subsequently assign a quality rating. If one or more return signals are deemed unacceptable (e.g., based on the corresponding quality rating), the return signal evaluation algorithm 214 can prompt the VD signal characteristic algorithm 212 to change one or more characteristics of the probed signal. For example, the return signal evaluation algorithm 214 can trigger the VD signal characteristic algorithm 212 to change the probed signal in response to the discovery that a certain number or percentage of return signals are unacceptable. The data acquisition process can then restart. Thus, the return signal evaluation algorithm 214 can initiate a feedback loop to optimize the characteristics of the probed signal.

[0070] VD signature processing algorithm 216 can be responsible for examining the data generated by signal sensor 208 to generate a biometric signature that can be used for authentication. As further discussed below, the biometric signature can be created during the following phases: (i) the registration phase, in which an individual joins an authentication program supported by authentication platform 202A, and (ii) the usage phase, in which an unknown person is authenticated by authentication platform 202A. At a high level, a biometric signature can indicate a measure related to vascular dynamics. One example of a biometric signature is a series of values ​​showing the change in the location, size, volume, or pressure of a blood vessel over time. Another example of a biometric signature is a series of values ​​showing the change in the velocity or acceleration of blood flowing through a blood vessel over time. While the term "biometric signature" can be used to refer to multiple values ​​corresponding to different points in time, a single value can also be used as a biometric signature. For example, the depth of a given blood vessel relative to the skin surface can be used as a biometric signature.

[0071] During the usage phase, authentication algorithm 218 can be invoked to perform authentication by comparing a new biometric signature generated for an unknown person with one or more reference biometric signatures associated with a given individual. As described above, the reference biometric signatures can be stored in biometric database 220. The output produced by authentication algorithm 218 can represent a final decision on whether an unknown person should be authenticated as a given individual. Authentication algorithm 218 can output discrete or continuous outputs, such as probability measures (e.g., specifying the probability that an unknown person is a given individual), binary outputs (e.g., authenticated or unauthenticated), classifications (e.g., specifying whether the unknown person was authenticated with low, medium, or high confidence), and so on.

[0072] Figure 2B This includes a high-level representation of System 200B, which can be used to authenticate the identity of unknown individuals based on gesture recognition. At a high level, Figure 2B System 200B is similar to Figure 2A The system operates in the manner described in system 200A. However, here, the authentication platform 200B determines whether to authenticate the unknown person based on the analysis of data generated by the signal sensor 208 when the unknown person performs a physical action (referred to as a "gesture"). Alternatively, this data may be based on or represent the output generated by the signal sensor 208 when the unknown person performs a gesture.

[0073] Therefore, biometric database 220 may include data related to the execution of a given gesture, as well as other data related to vascular properties. For example, biometric database 220 may include a “gesture signature,” which represents data related to the execution of a gesture by a given individual. This data may include one or more values ​​of vascular properties during execution. The gesture signature may be associated with anatomical location (and thus with one or more blood vessels) and / or electronic devices. As an example, the data may specify the change in the size of the radial artery when a given individual bends her wrist downwards and then upwards, determined by an electronic device mounted on the wrist. In this scenario, the gesture signature is associated with a specific gesture (i.e., bending the wrist downwards and then upwards), a specific blood vessel (i.e., the radial artery), and a specific electronic device (i.e., the electronic device mounted on the wrist).

[0074] A gesture can be any movement that requires tension or activation of one or more muscles and will result in changes in the blood supply to those muscles. These changes in blood supply will affect the vascular dynamics of blood vessels in the same anatomical region. Each gesture signature may include a vector of length N, where each element is an intensity value based on the magnitude of the frequency offset between the probe signal and the corresponding return signal. The frequency offset may be related to the dilation or constriction of the corresponding blood vessel, which in turn is related to the activation of one or more muscles during the execution of the gesture.

[0075] like Figure 2B As shown, the authentication platform 202B may include a gesture recognition module 222, operable to facilitate vascular dynamics-based authentication during gesture execution. The gesture recognition module 222 may include a gesture processing algorithm 224 and a gesture recognition algorithm 226. At a high level, these algorithms may be able to determine whether the data generated by the signal sensor 208 when an unknown person performs a gesture is sufficiently similar to data generated by the signal sensor 208 (or another signal sensor) during one or more previous executions. This approach allows the gesture recognition module 222 to determine whether the unknown person has performed a gesture as requested, thereby determining whether the unknown person should be authenticated as a given individual.

[0076] Gesture processing algorithm 224 can be responsible for examining the data generated by signal sensor 208 to generate a gesture signature that can be used for authentication. For example, gesture processing algorithm 224 can parse the data to identify a series of values ​​corresponding to the execution of a gesture, and then define a gesture signature based on this series of values. As further discussed below, gesture processing algorithm 224 can be responsible for generating gesture signatures during the registration and usage phases.

[0077] Conversely, gesture recognition algorithm 226 may be executed only during the usage phase. During usage, gesture recognition algorithm 226 may be responsible for identifying the gesture being performed based on analysis of data generated by signal sensor 208. For example, gesture recognition algorithm 226 may identify a gesture by comparing a new gesture signature generated for an unknown person with one or more reference gesture signatures stored in biometric database 220. Gesture recognition algorithm 226 may then produce an output indicating whether the unknown person performed the gesture, for example, as requested via UI 202. In some embodiments, authentication platform 202B determines whether to authenticate the unknown person as a given individual solely based on whether the output indicates that the gesture was performed by the unknown person. In other embodiments, authentication platform 202B takes a more nuanced approach. For example, authentication platform 202B may attempt to determine the similarity between the gesture performance of the unknown person and one or more past gesture performances of a given individual.

[0078] Figure 2C This includes a high-level representation of System 200C, which can be used to authenticate the identity of unknown individuals based on location recognition. At a high level, Figure 2C System 200C is similar to Figure 2A The system operates in the manner described in system 200A. However, here, the authentication platform 200C determines whether to authenticate an unknown person based on whether the signal scanner 208 is in the expected position relative to the unknown person's body.

[0079] In this embodiment, at least some biometric signatures in the biometric database 220 can be associated with values ​​indicating locations. These values ​​can be referred to as “location markers” or “location tags.” Location tags can include alphanumeric identifiers or descriptions of locations. Examples of suitable descriptions include “front right trouser pocket,” “back left trouser pocket,” “left chest,” and “right wrist.” Thus, each location tag can indicate which anatomical region the signal sensor 208 was located near when the corresponding biometric signature was generated.

[0080] like Figure 2C As shown, the authentication platform 202C may include a location recognition module 228, which is operable to facilitate authentication based on whether the vascular dynamics indicator signal sensor 208 is in the expected location. The location recognition module 228 may include a registration system 230, a location algorithm 232, and a location rule database 234.

[0081] The location rule database 234 may contain rules associated with each available location tag. These rules may be defined by users of the authentication platform 200C, administrators responsible for managing the authentication platform 200C, or the authentication platform 200C itself. As an example, a rule may specify purchase permissions, purchase conditions (e.g., a maximum purchase limit), or authentication conditions. Therefore, the location identification module 228 (and thus the authentication platform 200C) may prevent, restrict, or otherwise constrain authentication based on the rules in the location rule database 234. Rules may also be general instructions related to a specific computer program. As an example, a rule may define the criteria that must be met to play a song through a music-focused computer program, or a rule may define the criteria that must be met to start a fitness-focused computer program with a step count. Rules may be configurable (e.g., createable or editable) via the UI 202.

[0082] Registration system 230 allows an individual (e.g., a user or administrator) to assign rules to specific location tags and update these rules once they are stored in location rule database 234. As an example, a user might want to specify that transactions requiring authentication should be limited to a certain dollar amount (e.g., no more than $50) unless a given electronic device (e.g., a mobile phone) is in a given location (e.g., the left front trouser pocket). As described above, authentication platform 200C may be able to infer whether a given electronic device is in a given location by examining data generated by signal sensor 208 during the usage phase. More specifically, authentication platform 200C can analyze the data to determine whether its value indicates a blood vessel (e.g., the femoral artery) known to be near the given location.

[0083] Simultaneously, the location algorithm 232 can be responsible for retrieving location tags associated with the biometric signature from the biometric database 214 when necessary. Furthermore, the location algorithm 232 can use these location tags to retrieve appropriate rules from the location rule database 234.

[0084] Figure 3 An example of an electronic device 300 capable of implementing an authentication platform 314 is illustrated, designed to authenticate the identity of an unknown person based on data generated by a signal sensor 310. As described above, a signal transmitter 308 can emit a probe signal into the body of the unknown person, and the signal sensor 310 can generate data based on the reflection of the probe signal by physiological structures in the body. In some embodiments, the probe signal represents a radio wave, so the return signal detected by the signal sensor 310 can also be a radio wave. Note that the signal transmitter 308 can also be configured to emit a series of discrete probe signals, referred to as "pulses," at certain time intervals.

[0085] In some embodiments, the authentication platform 314 is embodied as a computer program executed by the electronic device 300. For example, the authentication platform 314 may reside on a mobile phone capable of acquiring data from which it can determine whether authentication is appropriate. As another example, the authentication platform 314 may reside on a wearable device capable of acquiring data from which it can make a determination. Examples of wearable devices include watches, fitness trackers, and head-mounted displays. In other embodiments, the authentication platform 314 is embodied as a computer program executed by another electronic device to which the electronic device 300 is communicatively connected. In such embodiments, the electronic device 314 may transmit data to other electronic devices for processing. For example, while authentication of an unknown person may be sought by a POS system used to initiate a transaction, the data may be generated by a mobile phone located near the unknown person. This data may be provided to the POS system or another electronic device (e.g., a computer server) for processing, or the data may be processed by the mobile phone before being delivered to the POS system or other electronic devices. Those skilled in the art will recognize that aspects of the authentication platform 314 may also be distributed among multiple electronic devices.

[0086] Electronic device 300 may include processor 302, memory 304, user interface (UI) output mechanism 306, signal transmitter 308, signal sensor 310, and communication module 312. Communication module 312 may be, for example, a wireless communication circuit designed to establish a communication channel with other electronic devices. Examples of wireless communication circuits include integrated circuits (also referred to as "chips") configured for Bluetooth, Wi-Fi, NFC, etc. Processor 302 may have general-purpose characteristics similar to a general-purpose processor, or processor 302 may be an application-specific integrated circuit (ASIC) that provides control functions to electronic device 300. Figure 3 As shown, the processor 302 can be directly or indirectly coupled to all components of the electronic device 300 to achieve communication purposes.

[0087] Memory 304 can be composed of any suitable type of storage medium, such as static random-access memory (SRAM), dynamic random-access memory (DRAM), electrically erasable programmable read-only memory (EEPROM), flash memory, or registers. In addition to storing instructions executable by processor 302, memory 304 can also store data generated by signal sensor 310 and data generated by processor 302 (e.g., when executing modules of authentication platform 314). Note that memory 304 is only an abstract representation of the storage environment. Memory 304 can consist of actual memory chips or modules.

[0088] As described above, the signal transmitter 308 can be configured to emit electromagnetic radiation, for example, in the form of radio waves, into the body of an unknown person to be authenticated. Typically, the signal transmitter 308 only emits electromagnetic radiation when instructed. For example, the authentication platform 314 can be configured to determine whether authentication is necessary. In this scenario, the authentication platform 314 can generate an output prompting the processor 302 to instruct the signal transmitter 308 to emit a probe signal with certain characteristics. As described above, these characteristics can be determined by the authentication platform 314.

[0089] The signal sensor 310 can be any sensing device capable of collecting reflections of probe signals from physiological structures in the body. Data that can be used to determine vascular dynamics can be based on, or represent, these reflections.

[0090] Communication module 312 can manage communication between components of electronic device 300. Communication module 312 can also manage communication with other electronic devices. Examples of electronic devices include mobile phones, tablet computers, personal computers, wearable devices, POS systems, and network-accessible server systems consisting of one or more computer servers. For example, in an embodiment where electronic device 300 is a mobile phone, communication module 312 can be communicatively connected to a network-accessible server system responsible for examining data generated by signal sensor 310.

[0091] For convenience, authentication platform 314 may be referred to as a computer program residing in memory 304. However, authentication platform 314 may consist of software, firmware, or hardware components implemented in or accessible to electronic device 300. According to the embodiments described herein, authentication platform 314 may include various algorithms and modules (collectively, “elements”), as referenced above. Figure 2A-2C These elements can be integral parts of the authentication platform 314. Alternatively, these elements can be logically separate from the authentication platform 314, but operate "together" with it. Together, these elements enable the authentication platform 314 to authenticate the identity of an unknown person based on an analysis of vascular dynamics determined from data generated by the signal sensor 310. As an example, after obtaining the data generated by the signal sensor 310, the authentication platform 314 can generate a biometric signature for an unknown individual to which the signal transmitter 308 emits pulse signals. The authentication platform 314 can then compare this biometric signature with at least one reference biometric signature associated with a given individual stored in the biometric database 316. The authentication platform 314 can determine whether to authenticate the unknown person as the given individual based on the degree of similarity between the biometric signature and the reference biometric signature. Figure 3 In this device, the biometric database 318 is located in the memory 304 of the electronic device 300. However, the biometric database 318 may alternatively or additionally be located in a remote memory accessible via a network by the electronic device 300.

[0092] Other elements may also be included as part of the authentication platform 314. For example, the UI module may be responsible for generating content to be output by the UI output mechanism 306 to be presented to unknown individuals. The form of this content may depend on the nature of the UI output mechanism 306. For example, if the UI output mechanism 306 is a speaker, then the content may include audio instructions to position the electronic device 300 near an anatomical area or to indicate that the authentication phase should continue. As another example, if the UI output mechanism 306 is a display, then the content may include visual instructions to position the electronic device 300 near an anatomical area or to indicate that the authentication phase should continue.

[0093] Authentication methods

[0094] Figures 4A-4B The document includes a flowchart of a procedure for authenticating users of an authentication platform based on radar detection of vascular dynamics, which are monitored using electronic devices located near the body. Figures 5A-5B Including flowcharts of these processes in the context of gesture recognition, and Figures 6A-6B This includes flowcharts of these processes within the context of location recognition. Note that, unless otherwise specified, the steps of these processes can be combined with steps of other processes. Thus, the authentication platform can utilize various aspects of vascular dynamics recognition, gesture recognition, or location recognition when responsible for authenticating unknown persons.

[0095] like Figures 4A-4B As shown, the authentication process has two phases: registration phase 400 and usage phase 450. These phases can be designed to allow hands-free authentication without requiring the user to interact with any electronic device.

[0096] To begin registration phase 400, the user may position the electronic device, including (i) a signal transmitter and (ii) a signal sensor, in an initial position. Note that in some situations, the user may need to place the electronic device in the initial position. For example, the user may need to hold the electronic device in the initial position. In other situations, the user may simply need to allow the electronic device to remain in its current position. For example, the user may be instructed to place the electronic device in a pocket where it is already located.

[0097] The user may be prompted by the electronic device or another electronic device to position the device in an initial location. For example, the user may indicate her interest in registering with the authentication platform through the interface displayed on the electronic device's screen. Generally, the initial location is near the anatomical region of interest (and thus near the blood vessel of interest). For example, the authentication platform may ask the user to position the electronic device in their front trouser pocket to be close to the femoral artery, or it may ask the user to position the electronic device near their wrist to be close to the radial artery.

[0098] In some embodiments, a user may have already authenticated themselves before the registration phase 400. For example, a user may be prompted to provide sensitive information (such as name, address, or payment card number) as a means of verifying their identity.

[0099] The authentication platform's VD signal characteristic algorithm can then generate a set of initial signal characteristics. In some embodiments, this set of initial signal characteristics represents default signal characteristics, while in other embodiments, it represents default signal characteristics modified based on conditions of registration phase 400. For example, the VD signal characteristic algorithm can modify the default frequency range used for scanning based on the initial location (and thus, based on the penetration depth that may be required). As another example, the VD signal characteristic algorithm can modify the default peak amplitude based on the minimum power requirements of the signal sensors included in the electronic device.

[0100] This set of initial signal characteristics can be provided to the signal transmitter included in the electronic device so that it can transmit a probe signal according to these initial signal characteristics. Thus, the signal transmitter can transmit the probe signal according to the strength, duration, timing conditions, and frequency range defined by the VD signal characteristic algorithm. Figure 4A As shown, the signal sensor then detects the return signal generated by the reflection of the probe signal within the body. This return signal can then be provided as input to either a return signal evaluation algorithm or a VD signature processing algorithm.

[0101] A return signal evaluation algorithm can be responsible for assessing the quality of the returned signal. For example, the algorithm can compare one or more attributes of the returned signal to a predefined benchmark to evaluate quality. These attributes may include amplitude, signal-to-noise ratio, and repeatability. For each benchmark, the algorithm can assign a value (e.g., between 0 and 1, between 1 and 100, etc.) based on the difference between the corresponding attribute and the benchmark. These values ​​can then be summed and divided by the total number of attributes to establish an overall quality rating for the returned signal. In some embodiments, weights can be assigned to these attributes. In such embodiments, some attributes may have a greater impact on the quality rating than others. If any value falls below a threshold defining a minimum acceptable standard, the algorithm can output a report indicating each attribute that fails to meet the corresponding benchmark. This report can be used by a VD signal characteristics algorithm to update the signal characteristics of the probe signal and then initiate another scan.

[0102] After obtaining an acceptable return signal, the VD signature processing algorithm can process the return signal to generate a biometric signature. For example, the VD signature processing algorithm can take the frequency difference (called a "frequency offset") between the probe signal and the return signal for each of N timestamped samples obtained within a certain time interval (also called a "measurement interval" or "scan interval"). These frequency offsets can then be normalized and stored in a vector of length N. In some embodiments, this vector also includes information such as the time "step" between each frequency offset. This vector can represent the biometric signature generated for the return signal.

[0103] like Figure 4A As shown, the quality rating generated by the return signal evaluation algorithm and the biometric signature generated by the VD signature processing algorithm can be stored in a biometric database accessible to the authentication platform. For example, the quality rating and biometric signature can be populated into entries in a data structure representing the biometric database. In some embodiments, the biometric database is maintained on the same electronic device as the authentication platform, while in other embodiments, the biometric database is maintained on a different electronic device than the authentication platform. For example, the authentication platform may reside on an electronic device located near the user's body, while the biometric database may reside on a network-accessible server system communicatively connected to the electronic device.

[0104] This process can be completed multiple times during Phase 400 registration. For example, a user can locate an electronic device in different locations to generate multiple biometric signatures, or a user can locate different electronic devices in the same or different locations to generate multiple biometric signatures. Regardless of how many biometric signatures are generated during Phase 400 registration, these biometric signatures can be stored in a biometric database for future authentication.

[0105] In phase 450, the user may be prompted to locate the electronic device at one of the predetermined locations for biometric signature defined in phase 400. For example, suppose a user is attempting to complete a transaction using a POS system. In such a scenario, the POS system can send a request to authenticate the user directly or indirectly to the authentication platform. For instance, the POS system can directly send the authentication request to the electronic device via a wireless channel established according to short-range communication protocols such as Bluetooth, Near Field Communication (NFC), or Wi-Fi. As another example, the POS system can send an instruction to a server system via a network that authentication is necessary, and the server system can then send the authentication request to the electronic device.

[0106] Upon receiving a request, the authentication platform can perform the steps described above regarding registration phase 400 to generate a biometric signature. As mentioned above, these steps may need to be performed once or multiple times to generate a biometric signature of acceptable quality.

[0107] Information about the electronic device or user to be authenticated can be used to retrieve one or more reference biometric signatures from a biometric database. For example, criteria such as the geographic location of the electronic device, its identifier, and the user's claimed identity can be used to identify one or more reference biometric signatures. The VD authentication algorithm can then compare the biometric signature to one or more reference biometric signatures. For example, the VD authentication algorithm can identify equivalent features between the biometric signature and the reference biometric signature. An example of a feature is a series of elements in two vectors for which the frequency offset is gradually increasing, indicating the start of a pulse. Starting from this equivalent feature, the value in each element of the biometric signature can be compared to the corresponding element in the reference biometric signature. The purpose of this is to identify differences in values ​​and / or the rate of change of values. This process can be repeated for one or more features that the VD authentication algorithm determines to be approximately or substantially equivalent between the biometric signature and the reference biometric signature. The VD authentication algorithm can then check these differences against acceptable tolerances. For example, if these differences fall within 0.25, 0.5, or 0.75 standard deviations, the VD authentication algorithm can consider the biometric signature sufficiently similar to the reference biometric signature.

[0108] VD authentication algorithms can make authentication decisions based on, for example, the number of differences and / or the average magnitude of deviations within acceptable tolerances. While authentication platforms may have varying degrees of confidence in their decisions, as mentioned above, authentication decisions are typically binary. In other words, an authentication decision usually indicates whether an authentication has been made or not. The authentication decision can be communicated by the authentication platform to the destination. For example, if the authentication request is submitted by a POS system as described above, then the authentication decision can be communicated to the POS system.

[0109] Figures 5A-5B The flowcharts include a registration phase 500 and a usage phase 550 in an embodiment where an authentication platform determines whether to authenticate a user based on gesture recognition. These flowcharts illustrate how the authentication platform can detect the execution of a gesture by changing the user's vascular dynamics based on these gestures.

[0110] At a high level, Figures 5A-5B Registration phase 500 and usage phase 550 and Figures 4A-4BThe registration phase 400 and the usage phase 450 are equivalent. However, here, the user can be prompted to perform a gesture when the electronic device is located near an anatomical area of ​​the body. The gesture can be selected (e.g., from several potential gestures) because performing the gesture may result in observable changes in the vascular dynamics of the blood vessels located in that anatomical area. For example, the gesture might require the user to tense their thigh muscles once per second over a period of several seconds, or the gesture might require the user to bend their wrist downwards and then upwards over a period of several seconds.

[0111] As described above, the signal transmitter can emit a probe signal with characteristics defined by the VD signal characteristic algorithm into the anatomical region, and the signal sensor can sense a return signal representing the probe signal reflected by physiological structures in the anatomical region. After the return signal evaluation algorithm determines that an acceptable return signal has been obtained, the gesture signature processing algorithm can process the return signal to generate a gesture signature. For example, the gesture signature processing algorithm can determine the frequency offset between the probe signal and the return signal for each of N timestamped samples obtained within the measurement interval. The frequency offset can then be normalized and stored in a vector of length N. As described above, this vector can also include information such as the time “step” between each frequency offset. If the user is asked to perform a known gesture, the frequency offset can also be associated with or “marked” with the respective gesture. This vector can represent the gesture signature generated for the return signal.

[0112] In registration phase 500, the gesture signature can simply be stored in a biometric database. However, in usage phase 550, the gesture signature can be compared by a gesture recognition algorithm with at least one reference gesture signature. Criteria such as the geographic location of the electronic device, the identifier of the electronic device, the user's claimed identity, and the gesture itself can be used to identify one or more reference gesture signatures. For example, the gesture recognition algorithm can identify equivalent features between the gesture signature and the reference gesture signature. An example of a feature is a series of elements in two vectors for which the frequency offset gradually increases, indicating the start of the gesture execution.

[0113] Starting with this equivalent feature, the value in each element of the gesture signature can be compared with the corresponding element of the reference gesture signature. This identifies similarities and differences in attributes, such as the timing of frequency transitions, rate of change, or amplitude. Differences in these attributes can be examined between vectors to determine if they fall within a certain tolerance (e.g., within 0.5 standard deviations). Based on these differences, the gesture recognition algorithm can generate a gesture confirmation indicating whether the gesture signature matches the reference gesture signature. In cases where the gesture confirmation indicates a mismatch, the authentication platform may not authenticate the user, or it may request the user to perform the gesture again (and then reuse stage 550).

[0114] Figures 6A-6B The flowcharts include a registration phase 600 and a usage phase 650 in an embodiment where an authentication platform determines whether to authenticate a user based on location identification. These flowcharts illustrate how the authentication platform assigns rules, settings, or functions to locate an electronic device in a given anatomical region by first identifying the location from vascular dynamics and then retrieving relevant rules from a biometric database.

[0115] like Figure 6A As shown, registration phase 600 can be related to Figure 4A The registration phase 400 is largely similar. However, after the biometric signature is generated, the user may be able to assign location tags to the biometric signature. The user, the authentication platform, or a separate computer program executed by the electronic device can then use the registration system to input one or more location rules for the newly assigned location tags. For example, the location algorithm can generate a function table for each location tag within the biometric database. These functions can be invoked via an application programming interface (API) to apply settings to a computer program executing on the electronic device or to invoke functions within that computer program.

[0116] In phase 650 of the usage phase, the certification platform can generate a biometric signature that can be compared with a reference biometric signature, as described above. Figure 4BAs discussed above, after a biometric signature is generated, the location algorithm can retrieve a location tag associated with the reference biometric signature from the biometric database. The location algorithm can then use the location tag to retrieve the corresponding location rule (if any) from the location rule database. These location rules can then be used by an authentication platform (e.g., to determine if authentication is appropriate) or by another computer program executing on the electronic device. For example, in the context of a transaction completed via a computer program, location rules can be used to determine whether the transaction meets certain criteria before authorization is granted. Additionally or alternatively, location rules can provide or facilitate a UI. For example, steps monitored by an electronic device can be activated by calling relevant functions via an API corresponding to a fitness-related computer program.

[0117] Figures 7A-7B This includes a flowchart of registration and usage phases 700 and 750, which is an example of certification procedures relying on vascular dynamics.

[0118] In registration phase 700, the UI may initially prompt the individual to locate the electronic device in an initial position (step 701). Generally, the UI is presented by the electronic device. For example, the electronic device may use sound or visual instructions to the individual to locate the electronic device in an initial position. However, the UI may be presented by another electronic device. For example, the UI may be used by the individual to indicate a mobile phone presentation indicating interest in completing registration phase 700, although the UI may instruct the individual to locate a wearable device (e.g., a watch or fitness tracker) in a given anatomical region (e.g., the wrist).

[0119] Subsequently, the authentication platform can determine the initial characteristics of the probe signal to be transmitted into the body (step 702). As described above, the initial characteristics can be based on the individual, the electronic device, or the initial location. The authentication platform can then cause (i) a signal transmitter to transmit the probe signal into the body, and (ii) a signal sensor to sense a return signal generated by the reflection of the probe signal (step 703). For example, the authentication platform can generate an instruction specifying the initial characteristics of the probe signal and then send that instruction to the operating system of the electronic device. Upon receiving the instruction, the operating system can operate the signal transmitter included in the electronic device to transmit the probe signal with the initial characteristics. Data representing the return signal generated by the signal sensor can be obtained by the authentication platform (e.g., via the operating system).

[0120] The authentication platform can evaluate the data (and thus the returned signal) to generate a quality rating (step 703). If the quality rating is below a threshold, the authentication platform can adjust the settings of the probe signal as described above. However, if the quality rating exceeds the threshold, the authentication platform can use the returned signal to generate a biometric signal (step 705). Furthermore, the authentication platform can store the quality rating and biometric signature in a biometric database (step 706).

[0121] In phase 750, the authentication platform initially receives input indicating an authentication request from the source (step 751). In some embodiments, the source is a computer program that executes on the same electronic device as the authentication platform. For example, if the authentication platform resides on a mobile phone, the authentication request may originate from a mobile application through which the user is attempting to perform an activity requiring authentication. In other embodiments, the source originates from another electronic device. For example, suppose a user attempts to complete a transaction using a POS system associated with a merchant. In this scenario, the POS system may require authentication, although the scan may be performed by an electronic device associated with the user.

[0122] Subsequently, the authentication platform can perform steps equivalent to steps 701-705 of the registration phase (step 752). Therefore, the authentication platform can generate a biometric signature related to the vascular dynamics of blood vessels in the anatomical region based on data generated by signal sensors included in an electronic device located near the user's anatomical region. The authentication platform can then retrieve at least one reference biometric signature from the biometric database (step 753). One or more reference biometric signatures can be associated with the individual to whom the user will be authenticated. Therefore, to determine whether to authenticate the user as that individual, the authentication platform can assess the similarity between the biometric signature and one or more reference biometric signatures (step 754). The authentication platform can then communicate the authentication decision to the source (step 755). As described above, the authentication decision can be based on the degree of similarity between the biometric signature and one or more reference biometric signatures.

[0123] Figure 8The illustration includes a scenario where authentication of an unknown person still occurs even though the electronic device is left in a front trouser pocket. In this embodiment, the vascular dynamics of the femoral artery are used as the sole means of identifying the unknown person. Those skilled in the art will recognize that in other embodiments, authentication can be based on the vascular dynamics of another conveniently located blood vessel. For example, if the electronic device used for scanning is located near the wrist or forearm, authentication can be based on the vascular dynamics of the radial or ulnar artery. As mentioned above, authentication can also be based on the vascular dynamics of multiple blood vessels. For example, if the electronic device used for scanning is located near the hip area (e.g., in a back trouser pocket), authentication can be based on the common iliac artery, internal iliac artery, and external iliac artery.

[0124] At a higher level, this approach allows for hands-free authentication because the authentication platform can define the characteristics of the probe signals emitted by the electronic device into the body of an unknown person. These characteristics can be generated algorithmically to allow information about vascular dynamics to be obtained by analyzing the return signals representing the probe signals reflected by physiological structures within the body. As mentioned above, the authentication platform can reside on the electronic device or another electronic device. For example, the authentication platform can reside on a network-accessible server system or POS system to which the electronic device is communicatively connected.

[0125] Typically, the detected signal represents electromagnetic radiation emitted in the 3.1–10.6 GHz frequency band, although in some embodiments the electromagnetic radiation may be outside this range. Therefore, as Figure 8 As shown, the detection signal can not only penetrate clothing before entering the body, but also penetrate to a certain depth through skin, fat, and muscle. Similarly, the return signal generated by the reflection of the detection signal may be able to penetrate muscle, fat, skin, and clothing to reach the electronic device. For these reasons, when performing authentication, an unknown person may be able to simply leave the electronic device in its normal location (e.g., in a front trouser pocket).

[0126] This authentication method has several advantages. First, it allows the use of electronic devices, including signal transmitters and sensors, without requiring conscious action to initiate or complete authentication. In fact, the method described herein may not require users to wear or place the electronic devices in locations that would disrupt normal daily use. Second, this method represents a novel means of locating electronic devices along the body's surface, namely, by examining the vascular dynamics of the scanned blood vessels. Third, while this method may allow the use of gestures as a means of authentication, these gestures do not need to be attention-grabbing or ostentatious. Instead, these gestures can be subtle (e.g., tensing muscles or moving limbs), thus remaining largely unnoticed by others.

[0127] Figure 9The illustration shows an example of a schematic implementation of an authentication platform 900 capable of authenticating an unknown person 902 based on the vascular dynamics of blood vessels. Initially, the authentication platform 900 receives input indicating a request for authentication of the unknown person (also referred to as the "user" of the authentication platform 900). Figure 9 In this embodiment, the request originates from an unknown person 902 within the POS system 904 used to complete the transaction. In other embodiments, as described above, the request may originate from elsewhere.

[0128] The authentication platform 900 can then instruct the unknown person 902 to position the electronic device 906 at a given location corresponding to the anatomical region of interest. This instruction can be presented by the POS system 904, or it can be presented by the electronic device 906. The manner in which the instruction is presented may depend on the deployment of the authentication platform 900. In other words, whether the instruction is presented by the POS system 904 or the electronic device 906 (or some other electronic device) may depend on where the authentication platform 900 resides.

[0129] After the electronic device 906 is positioned in a given location, it can transmit a probe signal toward the anatomical region of interest. Typically, the probe signal is generated according to instructions generated by the authentication platform 900. These instructions can specify characteristics of the probe signal, such as frequency, amplitude, samples per second, transmission timing, transmission duration, or any combination thereof. Furthermore, the electronic device 906 can generate a return signal representing the probe signal reflected by physiological structures within the anatomical region of interest.

[0130] Subsequently, the authentication platform 900 can examine the returned signal to determine whether to authenticate the unknown person 902 as a given individual. Specifically, the authentication platform 900 can generate a biometric signature indicating the vascular dynamics of blood vessels in an anatomical region of interest, and then compare this biometric signature with at least one reference biometric signature known to be associated with the given individual. The authentication decision can be based on the degree of similarity between the biometric signature and (or more) reference biometric signatures.

[0131] Alternatively or additionally, the authentication platform 900 may determine, based on the returned signal, whether the unknown person 902 performed the expected gesture or whether the electronic device 906 is in the expected location. Thus, the authentication platform 900 may determine whether to authenticate the unknown person 902 based on (i) vascular dynamics determined from the returned signal, (ii) the gesture determined from the vascular dynamics, or (iii) the location determined from the vascular dynamics.

[0132] After determining whether the unknown person 902 should be certified as the given individual she claims to be, the certification platform 900 can generate a notification indicating the certification decision. This notification can be sent to the POS system 904, which can then determine whether to allow the transaction based on the notification.

[0133] Figure 10 A flowchart depicts a process 1000 for biometric authentication of a person claiming to be a given individual. Initially, the authentication platform may receive input instructing the authentication of a person possessing an electronic device, which includes (i) a signal transmitter and (ii) a signal sensor (step 1001). The signal transmitter and signal sensor may be, for example, part of a UWBIC capable of generating and detecting signals across a frequency range extending over multiple GHz (e.g., 6 to 8 GHz, 6 to 8.5 GHz, 6 to 9 GHz). In some embodiments, the input is received from a computer program executed on the electronic device. In other embodiments, the input is received from another electronic device. For example, the input may be received from a POS system or a network-accessible server system with which the electronic device is communicatively connected.

[0134] Then, the authentication platform can cause the electronic device to emit a detection signal into the human body at a certain time interval (step 1002). For example, the authentication platform can determine appropriate characteristics of the detection signal based on the electronic device or the person, and then the authentication platform can cause the signal transmitter to emit a detection signal with appropriate characteristics. This can be done by sending an instruction to the operating system of the electronic device to operate the signal transmitter, which specifies the appropriate characteristics. Afterward, the authentication platform can acquire data representing the detection signal reflected by blood vessels located in the body (step 1003). This data can be generated by a signal sensor included in the electronic device emitting the detection signal, or the data can be based on the output generated by the signal sensor included in the electronic device emitting the detection signal.

[0135] The authentication platform can establish changes in the properties of blood vessels over the time interval based on this data (step 1004). Therefore, the authentication platform can determine how blood vessels change over time, rather than simply observing blood vessels in a "snapshot" corresponding to a single time point. This property could be (i) the size of the blood vessel, (ii) the location of the blood vessel, (iii) the flow rate of blood through the blood vessel, or (iv) the pressure exerted by the blood on the blood vessel.

[0136] Then, the authentication platform can determine the probability that the person is a given individual based on the analysis of attribute changes (step 1005). Thus, the authentication platform can determine whether to authenticate the person as a given individual based on the user's vascular dynamics determined from this data. For example, the authentication platform can compare attribute changes with reference values ​​included in a profile associated with the given individual, and then generate a score indicating the probability that the person is the given individual based on the similarity between the attribute changes and the reference values.

[0137] In some embodiments, the authentication platform is programmed to generate biometric signatures representing attribute variations as described above. In such embodiments, the authentication platform may examine a biometric database containing biometric signatures associated with different individuals and then identify a reference biometric signature associated with a given individual from among the biometric signatures. Each biometric signature in the biometric database may represent a reference value for an attribute established for the corresponding individual. The biometric database may be maintained in a data repository on an electronic device used for scanning the body, or it may be maintained in a data repository accessible via a network by the electronic device. To establish the probability that the person is the given individual (and thus determine whether to authenticate the person), the authentication platform may compare the biometric signature with the reference biometric signature.

[0138] Other steps may also be included. As an example, the authentication platform can infer from data that an electronic device is located near a specific anatomical region of the body. More specifically, the authentication platform can parse the data to identify patterns of values ​​indicating a given blood vessel known to be located in that anatomical region. For example, if the authentication platform detects values ​​representing the vascular dynamics of the femoral artery, it can infer that the electronic device is located near the hip. As another example, the authentication platform can receive a second input indicating confirmation that the person has been prompted to perform a gesture. In such an embodiment, the authentication platform can determine whether the person has performed the gesture based on analysis of the data. The authentication platform can determine that the person has performed the gesture in response to detecting patterns of values ​​in the data indicating the gesture.

[0139] Figure 11 A flowchart is depicted for a process 1100 for generating a biometric signature for an individual, which can then be used for authentication. Initially, the authentication platform may receive a first input indicating a request to create a biometric signature for the individual for authentication (step 1101). The first input may not require the creation of the biometric signature itself, but may simply represent a request to register the individual for authentication services provided, facilitated, or supported by the authentication platform.

[0140] The authentication platform may then present instructions to locate the electronic device near a specific anatomical region of the person's body (step 1102). In some embodiments, the individual may need to actively locate the electronic device near the anatomical region, while in other embodiments, the individual may be able to leave the electronic device in its current location. For example, if (i) the anatomical region is the front or side of the thigh, and (ii) the electronic device is already in a front or back trouser pocket, the electronic device may not need to be relocated. As another example, if (i) the anatomical region is the front or back of the wrist, and (ii) the electronic device is already attached to the wrist, the electronic device may not need to be relocated. As yet another example, if (i) the anatomical region is the chest region, and (ii) the electronic device is already in a breast pocket, the electronic device may not need to be relocated. Other examples of anatomical regions include the neck region (also referred to as the "neck region") and the temporal region, and these anatomical regions can be monitored using electronic devices such as network-connected necklaces (e.g., smart necklaces), headphones, and hearing aids. Subsequently, the authentication platform can enable electronic devices to emit detection signals into an individual's body (step 1103) and acquire data representing the detection signals reflected by blood vessels located in the body (step 1104). Figure 11 Steps 1103-1104 can be combined with Figure 10 Steps 1002-1003 are similar.

[0141] Subsequently, the authentication platform can provide the data as input to an algorithm that generates a biometric signature for the individual as output (step 1105). This biometric signature can represent one or more values ​​indicating temporal changes in vascular properties. As an example, the biometric signature can include a vector of length N, where each element is an intensity value based on the magnitude of the frequency offset between a probe signal and a corresponding return signal. In some embodiments, the biometric signature conveys information about the size of the blood vessel. In other embodiments, the biometric signature conveys information about the depth of the blood vessel under the skin, the velocity of blood flowing through the vessel, and the pressure exerted by the blood on the blood pressure. In other embodiments, the biometric signature represents the vibration curve of the blood vessel over the time interval during which a probe signal is emitted into the body.

[0142] The authentication platform can then store the biometric signature in a profile associated with the individual (step 1106). This profile can represent a single entry or multiple entries in a biometric database corresponding to the individual. This biometric database can be maintained on an electronic device used to scan the body and / or another electronic device communicatively connected to it.

[0143] These steps can be performed in various sequences and combinations. For example, if the authentication platform requires the electronic device to be relocated to another location or another electronic device to another location, these steps can be performed a second time. This is likely to ensure that authentication is robust to changes in the location of the electronic device. Therefore, the authentication platform can generate biometric signatures for different anatomical regions (and thus for different blood vessels), and these biometric signatures can be used for authentication individually or collectively in the future.

[0144] Figure 12 A flowchart depicts a process 1200 for determining whether to authenticate a person as a given individual based on vascular dynamics of blood vessels changing over time. Initially, the authentication platform may cause an instruction to be presented to position an electronic device near an anatomical region of the person to be authenticated (step 1201). This instruction may be auditory or visual in nature. For example, the instruction may audibly request the positioning of the electronic device near an anatomical region, or the instruction may visually request (e.g., via a text instruction or visual representation) the positioning of the electronic device near an anatomical region. In some embodiments, the instruction is presented by the electronic device, while in other embodiments, the instruction is presented by another electronic device (e.g., a POS system). Subsequently, the authentication platform may acquire data generated by a signal sensor disposed in the electronic device (step 1202). This data may represent a return signal based on the reflection (e.g., generated by the reflection) of a detection signal emitted by the electronic device towards the person over a certain time interval.

[0145] The authentication platform can estimate changes in the properties of blood vessels located in the anatomical region over the time interval based on this data (step 1203). The estimated changes may represent variations in the shape, location, flow rate, or pressure of the blood vessels over the time interval. The authentication platform can then determine whether to authenticate the person as the given individual based on a comparison of the estimated changes with a profile associated with that individual (step 1204). This profile may represent one or more entries in a biometric database. Each entry may include a reference value indicating the degree of change in that property when the given individual completes the registration phase. Therefore, the authentication platform can compare the estimated changes with at least one reference value to determine the likelihood that the person is the given individual.

[0146] Processing system

[0147] Figure 13 The block diagram illustrates an example of a processing system 1300, in which at least some of the operations described herein can be implemented. For example, components of the processing system 1300 may be housed in an electronic device including a signal transmitter and a signal sensor. As another example, components of the processing system 1300 may be housed in an electronic device including an authentication platform.

[0148] Processing system 1300 may include processor 1302, main memory 1306, non-volatile memory 1310, network adapter 1312 (e.g., network interface), video display 1318, input / output device 1320, control device 1322 (e.g., keyboard, pointing device, or mechanical input such as buttons), drive unit 1324 including storage medium 1326, or signal generation device 1330, all communicatively connected to bus 1316. Bus 1316 is illustrated as an abstract concept, representing one or more physical buses and / or point-to-point connections connected by appropriate bridges, adapters, or controllers. Therefore, bus 1316 may include a system bus, a Peripheral Component Interconnect (PCI) bus, a High Speed ​​PCI bus, a HyperTransport bus, an Industry Standard Architecture (ISA) bus, a Small Computer System Interface (SCSI) bus, a Universal Serial Bus (USB), and an Inter-Integrated Circuit (I2C) bus. 2 C) Bus, or a bus conforming to IEEE Standard 1394.

[0149] Processing system 1300 may share a similar computer processor architecture with computer servers, routers, desktop computers, tablet computers, mobile phones, video game consoles, wearable electronic devices (e.g., watches or fitness trackers), network-connected (“smart”) devices (e.g., televisions or home assistant devices), augmented or virtual reality systems (e.g., head-mounted displays), or other electronic devices capable of executing a set of instructions (sequential or otherwise) specifying actions to be taken by processing system 1300.

[0150] Although main memory 1306, non-volatile memory 1310, and storage medium 1324 are shown as a single medium, the terms "storage medium" and "machine-readable medium" should be understood to include a single medium or multiple media storing one or more sets of instructions 1326. The terms "storage medium" and "machine-readable medium" should also be understood to include any medium capable of storing, encoding, or carrying a set of instructions for execution by processing system 1300.

[0151] Generally, routines executed to implement embodiments of the present disclosure can be implemented as part of an operating system or a particular application, component, program, object, module, or sequence of instructions (collectively, a "computer program"). A computer program typically includes one or more instructions (e.g., instructions 1304, 1308, 1328) that are set in various memories and storage devices of a computing device at various times. When read and executed by processor 1302, the instructions cause processing system 1300 to perform operations to carry out various aspects of the present disclosure.

[0152] Although embodiments have been described in the context of a full-featured computing device, those skilled in the art will understand that various embodiments can be distributed as a program product in various forms. This disclosure applies regardless of the specific type of machine or computer-readable medium in which the distribution is actually caused. Further examples of machine and computer-readable media include recordable media, such as volatile and non-volatile memory devices 1310, removable disks, hard disk drives, optical discs (e.g., compact disk read-only memory (CD-ROM) and digital versatile disks (DVDs)), cloud-based storage, and transport media, such as digital and analog communication links.

[0153] Network adapter 1312 enables processing system 1300 to perform data mediation with entities outside processing system 1300 in network 1314 via any communication protocol supported by processing system 1300 and external entities. Network adapter 1312 may include a network adapter card, wireless network interface card, switch, protocol converter, gateway, bridge, hub, receiver, repeater, or transceiver including integrated circuits (e.g., enabling communication via Bluetooth or Wi-Fi).

[0154] Notes

[0155] The foregoing description of various embodiments of the claimed subject matter is provided for illustrative and descriptive purposes. It is not intended to be exhaustive or to limit the claimed subject matter to the precise forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to best illustrate the principles of the invention and its practical application, thereby enabling those skilled in the art to understand the claimed subject matter, the various embodiments, and the various modifications suitable for the particular intended use.

[0156] While the “Detailed Description” section describes certain embodiments and contemplated best modes, the technology can be implemented in many ways, however detailed the “Detailed Description” section may appear. Embodiments can vary considerably in their implementation details, yet are still covered by this specification. Specific terms used in describing certain features or aspects of the various embodiments should not be construed as implying that such terms are hereby redefined as limited to any specific characteristic, feature, or aspect of the technology associated with that term. Generally, the terms used in the following claims should not be construed as limiting the technology to the specific embodiments disclosed in the specification, unless such terms are expressly defined herein. Therefore, the actual scope of this technology covers not only the disclosed embodiments but also all equivalent ways of implementing or carrying out the embodiments.

[0157] The language used in this specification has been chosen primarily for readability and guidance purposes. It may not have been chosen to be limiting or restrictive of the subject matter. Therefore, it is intended that the scope of this technology not be limited by this "Detailed Description" section, but rather by any claims granted under the application based thereon. Thus, the disclosure of various embodiments is intended to be illustrative, and not to limit the scope of the technology recited in the claims.

[0158] Practicality of work

[0159] This disclosure can be applied to biometric authentication in computer security.

Claims

1. A method for authenticating a person as a given individual, the method comprising: The electronic device receives input indicating a request for authentication of the person possessing the electronic device; The electronic device transmits signals into the person's body at certain time intervals; The electronic device acquires data representing signals reflected by blood vessels located in the body; The electronic device infers, based on the data, that it is located in a pocket of clothing near an anatomical region of the body, the inference being based on a pattern of values ​​included in the data that indicates a given blood vessel known to be located in the anatomical region; The electronic device determines the changes in the properties of the blood vessel within the time interval based on the data; and The electronic device determines the probability that the person is a given individual based on the analysis of changes in the attribute.

2. The method of claim 1, further comprising: The electronic device receives a second input indicating confirmation that the person has been prompted to perform a gesture; and The electronic device determines whether the person performed the gesture based on the analysis of the data.

3. The method as described in claim 2, wherein, The person performed the gesture in response to the discovery of a pattern in the data that indicates the second value of the gesture.

4. The method of claim 1, wherein, The determination includes: The change in the attribute is compared with a reference value included in the profile associated with the given individual, and A score is generated based on the similarity between the changes in the attribute and the reference value, indicating the probability that the person is the given individual.

5. The method of claim 4, wherein, The attributes include (i) the size of the blood vessel, (ii) the location of the blood vessel, (iii) the flow rate of blood through the blood vessel, and / or (iv) the pressure exerted by the blood on the blood vessel.

6. The method of claim 4, wherein, The profile is maintained on a network-accessible server system, and the electronic device is communicatively connected to that system.

7. The method of claim 1, wherein, The data is generated by an ultra-wideband integrated circuit in the electronic device, which is capable of detecting signals in a frequency range extending from 6 GHz to 9 GHz.

8. The method of claim 1, wherein, The determination includes: Examine the biometric database that stores biometric signatures associated with different individuals. Each biometric signature represents a reference value for the attribute established for the corresponding individual. Identify a reference biometric signature associated with the given individual from the biometric signatures included in the biometric database, and The change in the attribute is compared with the reference biometric signature to establish the probability.

9. The method of claim 8, wherein, The biometric database is maintained in a data repository accessible via a network by the electronic device.

10. The method of claim 1, wherein, The input is received from a payment system, which the person uses to initiate a transaction that requires authentication.

11. The method of claim 1, wherein, The anatomical region is the front or side of the thigh, and the blood vessel is the femoral artery.

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