2254 results about "Biometric trait" patented technology

A programmatically generated AI avatar includes a customizable personality module, acting as the embodied interface for a powerful AI “mind” that delivers personalized coaching to improve user health, well-being, and longevity. The system uses machine learning, large language models, and biometric modeling to synthesize real-time, multi-modal health data—including sleep, nutrition, glucose, mood, and activity—and generate forward-prescribed KHAs. Unlike human coaches, it continuously adapts based on context and behavior, targeting the root cause: metabolic dysfunction—namely by restoring healthy, sustainable body composition through the preservation or building of lean muscle mass and reduction of excess fat. KHAs can also be shared with friends or programmatically generated AI avatars, allowing for coordinated action, emotional support, and accountability through social connection—further reinforcing positive behavior and adherence. The system's reinforcement learning engine incorporates both individual response data and anonymized population-level insights to optimize recommendations over time, learning which interventions are most effective for users with similar physiological and behavioral profiles. First validated with Olympic athletes—resulting in measurable improvements and medal-winning outcomes—this system offers a scalable, emotionally intelligent coaching engine that exceeds human capability, designed for the ultimate purpose of supporting sustainable health, resilience, and human thriving.

A system and method for person identification from video data by disentangling biometric identity features from non-biometric appearance and activity features are disclosed. The system processes RGB video sequences depicting individuals performing various activities to extract spatio-temporal features. These features are separated into distinct biometric identity representations and non-biometric features related to appearance and performed activities. To achieve this separation and minimize appearance bias, the system utilizes an auxiliary supervisory model. At least two implementations of this supervisory model are disclosed: one using semantic supervision via structured embeddings processed through a vision-language model, and another employing silhouette-based feature distillation from a silhouette-trained neural network. Joint training for biometric identification and activity classification ensures accurate identification of individuals independently of facial visibility, clothing differences, or activity variations.

The invention discloses a shortest-time-based bird flock repelling effect evaluation method for an airport intelligent bird repelling system, and the method comprises the steps: S1, collecting the multi-dimensional monitoring data of an airport bird flock repelling process through a biological feature sensing system, and enabling the multi-dimensional monitoring data to comprise a repelling duration parameter, a region clearing index, and bird flock adaptability response data; s2, based on the multi-dimensional monitoring data, analyzing the spatio-temporal behavior characteristics of the bird flock through an intelligent analysis terminal, and constructing a multi-dimensional bird flock expelling effect characteristic matrix; s3, based on the feature matrix, performing dynamic weight optimization of bird flock expelling effect evaluation indexes with the shortest expelling time as a target, and generating a self-adaptive weight coefficient group; and S4, based on the self-adaptive weight coefficient group, performing panoramic evaluation on the bird flock repelling effect by adopting a time sequence perception memory network, and outputting a repelling efficiency analysis result taking the repelling timeliness as a core. While the flight safety is guaranteed, efficient utilization of bird repelling resources is realized.

The invention discloses an MES (Manufacturing Execution System) login authentication method based on a face recognition technology, which relates to the technical field of industrial information security, and comprises the following steps of: constructing an authentication data acquiring a face image of an authorized user of an MES, extracting a reference face feature template after preprocessing, and constructing the authentication database; performing associated storage on the reference feature template, user identity information, preset operation authority and affiliated production station information to form an authentication database; according to the method, spoofing attacks are prevented through multi-mode living body detection, and the problem that a traditional password / key is easy to leak and borrow is solved in combination with uniqueness of human face biological characteristics; a person-post-terminal binding mechanism further limits permission abuse, and internal operation risks are reduced; the non-contact authentication does not need to memorize a password or carry a physical key, and is suitable for a working scene that a workshop operator wears gloves and holds tools; and the recognition passing rate in a complex industrial environment is improved through dynamic threshold optimization.

A system and method for processing genomic data using dynamic latent manifolds that transforms multi-modal genomic datasets into geometric representations within a curved manifold space. The system receives genomic datasets including DNA sequences, genetic variants, and expression data, then extracts biological features and assesses importance using trained neural networks. Manifold curvature values are computed based on biological significance, and genomic data is embedded as geometric structures where semantic relationships are represented through distance and curvature properties. The system generates compression pressure fields that influence processing decisions and computes optimal geodesic paths through the manifold to minimize cognitive action functionals. Adaptive compression rates are determined for different genomic regions based on geometric properties and biological importance. The manifold structure evolves through use, strengthening frequently accessed pathways while applying thermodynamic decay to unused concepts. The system supports hierarchical organization across biological scales, reversible navigation, and federated learning capabilities that enable privacy-preserving collaboration.

The invention discloses a dynamic network access control method and system based on a zero-trust architecture, and the method comprises the steps: integrating equipment health degree evaluation through the triple dynamic binding of biological feature dynamic binding, equipment fingerprint salt value hash verification and environmental state perception, and constructing a real-time trust basis; dynamic risk quantification is realized based on a multi-source heterogeneous data fusion machine learning model, real-time upgrading and degrading self-adaptive adjustment of authority is realized through an AI driving strategy generation module according to a real-time risk score, a zero-trust sandbox limitation sensitive operation is triggered for high-risk access, and a minimum authority channel is started for low-risk access; performing fine-grained access control and intercepting an unauthorized request in real time by adopting an agent-free API gateway technology, and monitoring an operation behavior in combination with a block chain non-tampering storage access log and an anomaly detection algorithm; finally, a continuous self-adaptive evolutionary cycle is formed through a risk assessment-policy execution-abnormal feedback closed loop mechanism, and the static lag problem of traditional network access control is systematically solved.

A symbolic neuroadaptive control system is disclosed for real-time arbitration, consent, and ethical modulation of artificial intelligence agents operating in wearable computing environments. The system integrates multimodal biometric telemetry—including high-resolution EEG signals—with a symbolic kernel that performs logic-driven arbitration over cognitive, emotional, and ethical states. Using Coq-verified invariants and zero-knowledge biometric consent tokens, the system constructs a deterministic symbolic execution graph, gating AI outputs based on internal user states such as trauma, stress, or intentionality. Unlike conventional black-box BCI models, the invention routes EEG-inferred affective-symbolic tokens through a formal ethics layer that enforces real-time interrupt control, utility bounding, and trust verification. The kernel enables AGI systems to defer or modify behavior based on user-state alignment, granting sovereign agency over all downstream actions. This neuro-symbolic architecture redefines the interface between human cognition and intelligent machines, enabling emotionally conscious, morally verifiable, and symbolically transparent AI governance in dynamic, high-stakes contexts.The present invention relates to artificial intelligence and neurotechnology, specifically to a real-time, neuro-symbolic operating system kernel that converts electroencephalography (EEG) signals into structured symbolic data for use in emotional cognition, ethical prioritization, autonomous agent dispatch, and real-time telecommunications routing. The invention bridges brain-computer interface (BCI) inputs with symbolic AI architectures to enable ethically aligned machine response during cognitively or emotionally intense events.

A method for secure onboarding to a closed mesh network in which a camera-equipped device scans a QR code embedded in a durable physical carrier (e.g., adhesive bandages, photographs, ID cards) to extract an access credential, configuration profile, or secure URL that automatically configures the device for network access, authenticates the device to the mesh without manual credential entry or transmission of plaintext credentials over insecure channels, and establishes end-to-end encrypted communications between network nodes; the method supports cryptographically random, single-use or time-limited credentials with periodic refresh to prevent replay, encoding of VPN / WireGuard or proprietary configuration profiles, substantially real-time onboarding (e.g., under five seconds), optional multi-factor verification (biometric or PIN), immediate initiation of secure messaging, voice / video or data sessions upon onboarding, audit logging for compliance and revocation based on detected unauthorized activity, and covert or overt embedding of QR codes in environment-resistant carriers for emergency, disaster response, first responder, or covert deployment, with the QR code rendered unreadable or deactivated after successful onboarding to prevent credential reuse.

The invention is suitable for the technical field of intelligent door locks, and provides a voice interaction processing method, device and system, an intelligent door lock and a cloud server, and the method comprises the steps: receiving a voice instruction sent by a user, and carrying out the local recognition, so as to obtain a local recognition result and the confidence of the local recognition result; when the local recognition result is matched with the local instruction set and the confidence is high, executing a local operation corresponding to the local recognition result; otherwise, establishing a secure communication session with the cloud server based on voiceprint biological characteristics extracted from the voice instruction; sending the voice instruction to a cloud server through the secure communication session, and receiving a cloud operation instruction and / or a dynamic response text returned by the cloud server; and executing corresponding operation according to the cloud operation instruction, and / or converting the dynamic response text into voice for broadcasting through a text-to-voice engine. The intelligent door lock voice interaction system solves the problem that the real-time performance, the reliability, the safety and the interaction intellectualization are difficult to balance in the existing intelligent door lock voice interaction technology.

Systems and methods for performing zero knowledge proofs to prove a user's possession of secret data and / or biometric data without exposing such data. The methods can include receiving a certificate signing request and biometric data associated with a user; creating a stable key based at least in part on the biometric data; creating a private key based at least in part on the stable key; transmitting the private key to the user device for local storage thereon; creating a public key based at least in part on the private key; and forwarding the certificate signing request to an issuer.

The invention relates to a method and system for encrypting and isolating storage data of a credential mobile terminal, and belongs to the technical field of information. The method comprises the following steps: encrypting and partitioning the whole storage area, and setting an access strategy according to user permission; when a user requests to access, executing multi-factor authentication containing a password and an external hardware certificate, and adding biological characteristic authentication; after the authentication is passed, an encryption key is taken from the domestic security chip; and encrypting and decrypting data by using the secret key, and opening corresponding partition access according to authority. The system comprises a storage partition module, an identity authentication module, a key management module, an encryption and decryption module and an authority control module and is used for executing the method. The method also comprises the steps of equipment startup trusted boot and firmware upgrade verification, is based on a custom curing system, and is compatible with Android 10 +. The problems of poor hardware controllability and positioning security risk of the creative mobile terminal are solved, and full-link data security protection is realized.

The invention relates to the technical field of biological feature recognition, and particularly provides an anti-migration PPG recognition method based on rate perception and a state space model. The method comprises the following steps: performing physiological feature front-end extraction on an original single-channel PPG signal to obtain a high-dimensional shallow feature sequence; performing double-flow cooperative processing on the high-dimensional shallow-layer feature sequence, and distributing the high-dimensional shallow-layer feature sequence to two parallel branches, namely a control flow branch and a data flow branch; in the control flow branch, an amplitude spectrum and an instantaneous physiological rate curve are obtained; in the data stream branch, acquiring a deep global feature sequence with rate invariance; obtaining multi-scale refinement features based on the high-dimensional shallow feature sequence and the deep global feature sequence; according to the multi-scale refinement features, a final biological feature recognition result is obtained, the method can actively sense the physiological rate change, and efficient nonlinear modeling can be achieved with the extremely low parameter quantity.

The invention belongs to the technical field of network security, and particularly relates to a network security protection system and method based on an electric power emergency communication environment, and the system comprises a quantum-classical hybrid encryption system which is used for generating a dynamic key in real time; the biological characteristic driven dynamic trust ring is used for equipment identity authentication; the unmanned aerial vehicle relay network protocol is used for data transmission; the secret key after-reading burn-down protocol is used for information secret key after-reading burn-down; a key output by the quantum-classical hybrid encryption system is used for authentication encryption of a biological characteristic driven dynamic trust ring, an unmanned aerial vehicle relay network protocol transmits data encrypted by the quantum key, and a key burn-down protocol is used for reading encrypted data to trigger a key burn-down mechanism. According to the method, the key security is improved, the network attack difficulty is increased, the communication delay in a disaster is reduced, man-in-the-middle capture and attack can be immunized, and the problem of contradiction between the encryption strength and the real-time performance is solved.

The embodiment of the invention discloses a cross-modal biological feature generation type enhancement method and system. The method comprises the following steps of: constructing a cross-modal biological feature recognition model, respectively acquiring long-distance modal data and short-distance modal data of a user, extracting feature vectors, pre-recognizing the user based on the long-distance feature vectors, finely recognizing the user based on the short-distance feature vectors, and carrying out mutual complementation and fusion on various modal features, so as to improve the recognition accuracy of the user. According to the method, the limitation that a single mode is low in recognition accuracy and prone to being interfered under different distance scenes is overcome, the identity recognition safety is improved, and through a secondary recognition mechanism combining pre-recognition and fine recognition, the efficiency and precision of identity recognition are improved while the system load is reduced.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for medical condition identification. One of the methods includes obtaining visual data representing at least one body part of a subject; obtaining non-visual data that corresponds to one or more biological characteristics of the subject; providing data representing (i) the visual data and (ii) the non-visual data to one or more machine learning models, wherein the one or more machine learning models are trained to predict presence of a medical condition; obtaining an output of the one or more machine learning models that is generated based on the one or more machine learning models processing the visual data and the non-visual data; and determining presence of the medical condition for the subject using the output of the one or more machine learning models.

The invention provides a driving authority management method and device based on biological feature verification and a medium, and belongs to the technical field of vehicles. The method comprises the steps of collecting facial features, voiceprint data and driving behavior data by detecting a starting operation of a current driver; the identity of the current driver is recognized by using a multi-modal biological recognition algorithm, and the accuracy and anti-counterfeiting capability of identity verification are improved by adopting a bimodal fusion scheme of face recognition and voiceprint recognition; when it is determined that identity recognition of the current driver succeeds, a driving account is determined to judge whether the driver has the use permission or not, a binding relation of driver identity-account-function permission is established, and accurate matching of the intelligent driving permission and the driver qualification is achieved; the open state of the intelligent driving function is dynamically controlled based on a permission verification result, if the permission exists, the intelligent driving function is automatically prepared, if the permission does not exist, the function is locked, a clear prompt is given, and the safety risk that the intelligent driving function is used before being learned is avoided from the source.

The invention discloses an Internet enterprise multi-mode identity verification method and system, and belongs to the technical field of Internet enterprise security, and the method comprises the steps: obtaining user historical behavior data, current transaction request data, equipment environment parameters and initial biological signal data, carrying out the risk assessment, and generating a verification path; obtaining a personalized verification sequence instruction, collecting a user face dynamic video stream, a real-time voice stream and response action time sequence data, carrying out cross-modal association comparison with a user reference biological feature template, outputting a biological feature confidence matrix, carrying out association analysis in combination with the obtained structured identity feature vector and risk assessment, and obtaining a personalized verification result; and generating a verification decision feature vector to judge a verification result state, and obtaining a pass instruction, a rejection instruction or a manual auditing request instruction. According to the method, dynamic risk-driven multi-modal verification path generation, cross-modal biological feature association decision and incremental learning mechanisms are adopted, so that the optimal balance between security and user experience can be realized in a complex network environment.

The invention provides an identity authentication method which can be applied to the technical field of biological recognition. The identity authentication method comprises the following steps: after agreement or authorization of a user is obtained, collecting biological characteristic data of multiple modes of the user; preprocessing the collected biological characteristic data of various modes to generate corresponding biological characteristic vectors; performing quality evaluation on each biological feature vector to generate a corresponding quality score; dynamically calculating a weight coefficient of each biological feature vector in a feature fusion process by using a nonlinear weighting function based on the quality score; based on the weight coefficient, performing feature level fusion on each biological feature vector to generate a primary fusion feature; and performing cross-modal correlation analysis on the primary fusion features by using a multi-branch convolutional neural network based on an attention mechanism, and outputting an identity authentication result. The invention also provides an identity authentication device, equipment, a medium and a program product.

To verify the identity and non-alteration of a document prepared in relation to a prescribed activity or operation by a qualified person in a certain site or organization in another site or organization.SOLUTION: A system according to claim 1, further comprising: a first device configured to execute a process of acquiring biometric information; and a second device configured to execute a process of performing biometric authentication using the biometric information and a process of determining whether or not a person authenticated as the principal is qualified to perform a predetermined activity or an operation, wherein the first device performs an authentication process on a document created in relation to the predetermined activity or the operation of the person determined to be qualified by the second device. The information processing apparatus further includes a third apparatus that further executes a process of creating a signature using the biometric information for signature of the individual and executes a process of registering the document and the signature created by the first apparatus in a first storage unit, and a fourth apparatus that executes a process of verifying correctness of a set of the document and the signature registered in the first storage unit of the third apparatus using a public key for verifying the signature.SELECTED DRAWING: Figure 1

A computer-implemented method of registering a user identifier in a public key infrastructure comprising a trusted device and a server is provided. The method includes the steps: receiving the user identifier as an input to the trusted device; obtaining a hash value of the user identifier; the trusted device obtaining biometric data comprising a facial image of the user; generating a public key and a privacy preserving data structure using the biometric data, encrypting the user identifier using the public key, and storing the encrypted user identifier as metadata in the privacy preserving data structure, wherein a private key for decrypting the encrypted user identifier can be generated from the privacy preserving data structure using subsequently acquired biometric data comprising the facial image of the user; generating a device token corresponding to the trusted device and obtaining a hash value of the device token, the server storing the privacy preserving data structure uniquely indexed by the hash value of the device token while using the hash value of the user identifier as a primary key, and the trusted device storing the device token.

The invention discloses a sample sampling inspection process management method and a related device, and the method comprises the steps: firstly obtaining the sampling task information of a target sampling person, generating a unique task identifier, then collecting the biological characteristic data, and generating an electronic sampling list in combination with the unique task identifier; determining a to-be-sampled target sample according to the electronic sampling list, and respectively obtaining first and second sample identification information of the target sample; verifying the target sample, and if the information is consistent, generating a seal data packet for sealing the sample; acquiring position information when the target sample is sent; obtaining a target analysis result of the seal data packet; and determining a target sample according to the target analysis result and the position information to complete sample sampling inspection operation. Therefore, the sample sampling inspection process is managed based on the unique task identifier, the risk of sampling inspection task confusion and the like can be reduced, and the sampling inspection process systematicness and the error-proofing capability are improved.

The invention relates to the technical field of intelligent security and protection, and discloses an intelligent face recognition and early warning system for monitoring, which comprises a data acquisition module, a feature processing engine, a decision center module and an early warning execution terminal, and is characterized in that visible light and infrared thermal imaging bimodal face data are acquired through a multispectral imaging unit; stable extraction of biological characteristics in an extreme environment is realized; a target path is pre-judged by fusing the real-time track and a historical behavior mode through a long short-term memory network; the decision center dynamically triggers a three-level early warning protocol according to the abnormal level, and links the acousto-optic equipment, the access control system and the mobile terminal to execute blocking operation; and deploying an edge computing node and a cloud collaborative architecture to realize model increment optimization, and adaptively correcting an early warning threshold according to the personnel density gradient by using a dynamic weight adjuster. The problems that a traditional system fails in recognition in a shielding environment, lacks behavior pre-judgment ability and is rigid in early warning mechanism are solved.

Object description information that is transmitted by a biometric recognition apparatus is received, the object description information includes a biometric feature of a target object and location information of the target object. Identity information of the target object is obtained according to the biometric feature. A service information set in association with the identity information is obtained. From the service information set, one or more pieces of candidate service information are selected. The one or more pieces of candidate service information are transmitted to a terminal device associated with the identity information. At least a first piece of target service information returned by the terminal device is received. At least a first service corresponding to the first piece of target service information is processed. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also contemplated.

The invention provides an automobile cabin end side model multi-modal perception enhancement method, which comprises the following steps: S1) deploying an RGB camera in a cabin, and collecting facial biological characteristic data, hand interaction behavior data and cabin overall environment data of a driver; s2) adjusting image illumination and contrast by adopting an algorithm, dynamically focusing a key area through a target detection frame to process a shielding problem, and eliminating static redundant information; s3) constructing a neural network, extracting facial biological features and the like in parallel, and outputting low-dimensional feature vectors; s4) time sequence association is established, key area feature weights are enhanced through a space attention mechanism, and a logic relationship among different features is explicitly modeled; s5) aggregating the weighted feature maps by using feature attention pooling, and retaining a core feature channel in combination with a channel pruning technology to realize model compression; s6) dynamically adjusting the calculation priority and weight distribution of each feature extraction branch; and S7) the end side uploads the sample to the cloud side, and the cloud side generates an update package and pushes the update package to the end side to complete model iteration.

A system for authenticating a user includes a memory device, and a processor. The processor is configured to receive a first biometric sample associated with a user from a biometric measurement device, and receive, substantially contemporaneously with receiving the first biometric sample, an image of a user credential, where the image includes credential data, such as any of a variety of personally identifiable information. The processor may also be configured to compare the received first biometric sample to at least one of: i) a second biometric sample included in the credential data, or ii) a third biometric sample stored by an authentication database in association with the user. Likewise, in at least some embodiments, the processor may determine whether to authenticate the user, such as based upon the comparison.

A node-edge symbolic consent kernel (NESCK) provides a computing architecture in which every instruction is gated by a verifiable human-intent signal and an ethical-predicate chain prior to execution. The system integrates a biometric-sensing front-end (EEG / GSR / facial micro-affect), a symbolic arbitration engine that transforms bio-intent data into consent tokens, and a cryptographically bonded node-edge ledger that records execution lineage, revocation, and audit proofs. Each node represents an executable state bound to a human consent fingerprint, while each edge encodes the ethical transition rules authorizing propagation through the network. At runtime, the kernel evaluates symbolic predicates, verifies zero-knowledge proofs of consent, and allows or halts instruction dispatch. The framework operates across devices, edge nodes, and cloud layers, enabling real-time lawful AI behavior, revocable autonomy, and tamper-proof moral audit trails. Embodiments span neuroadaptive wearables, autonomous vehicles, robotics controllers, and sovereign AI systems requiring continuous consent and transparent accountability.

A method and system are disclosed for creating a graphic element representing encoded biometric data that represents the facial biometrics of two or more individuals. The method includes capturing facial images of each person, generating non-reversible face-derived feature vector data, and embedding this data into a graphic on the item. To validate that the graphic is derived from the people presenting the graphic, the graphic is processed to extract corresponding feature vector data, which is compared to the data derived from newly collected facial images of the presenting people. Validation indicators are based on threshold comparisons. The system supports verification using photographs on the item and enables secure association through trusted entities and cryptographic signatures. The graphic may encode alpha-numeric strings representing biometric and document data. The graphic may be applied to any document or item, including driver's licenses, passports, titles, ballots, and financial instruments.

The invention relates to the technical field of data processing, and discloses a television terminal education application multi-mode user authentication method and system. The method comprises the following steps: monitoring a user face orientation offset degree and a voice interaction frequency through an IPTV set top box to obtain a learning participation degree index, comparing the index with a preset threshold 0.7 to execute a concentrated learning or distraction state authentication process, and dynamically adjusting through an authentication severity regulator to obtain a high-sensitivity or low-sensitivity authentication parameter, and performing weighted fusion calculation on the multi-modal biological characteristics of the user to obtain an identity confidence score associated with a learning state, and performing matching verification on the identity confidence score and a dynamic authentication threshold to obtain a user identity authentication result based on the learning concentration degree. According to the method and the device, the problem that a user authentication strategy in television terminal education application cannot adapt to dynamic change of a learning state is solved, and the accuracy of multi-modal user identity authentication based on learning concentration and the adaptability of user learning experience are improved.

The invention discloses a door lock and multi-helper cooperation method and system, and the method comprises the steps: generating a delegation request message according to the operation that a plurality of delegators select helpers on respective door locks and input shopping demands, and building a delegation relation between the delegators and the helpers; according to the established delegation relationship, the delegator door lock sends an improved multicast joining message to a preset multicast address; the shop door lock sends a list request message according to an event triggered by face swiping of the assistant in the unmanned shop, and the router carries out hierarchical aggregation on shopping lists of the plurality of consignors according to a biological feature matching result, generates an aggregated shopping list PIM message and forwards the aggregated shopping list PIM message to a shop system; and according to the aggregated shopping list provided by the store system, the assistant completes commodity selection and purchase, and each consignor respectively confirms and completes payment. By utilizing the embodiment of the invention, the full-process automation from entrustment establishment and list automatic aggregation to distributed payment can be realized, and the efficiency and convenience of multi-person collaborative shopping are improved.

The invention relates to the field of cryptology, and discloses a two-factor authentication key negotiation method based on biological characteristics, a user holds double authentication factors, namely a private key authentication factor and a biological characteristic authentication factor, a server holds secret information, and the private key authentication factor and the biological characteristic authentication factor perform mutual authentication and form a shared key, so that the service can be obtained. A user can pass authentication only by holding two authentication factors at the same time, and when an enemy steals one authentication factor or secret information of a server, the enemy cannot disguise that the user passes the authentication of the server; even if an enemy steals the two authentication factors of the user, the enemy cannot be disguised as a server to pass the authentication of the user, so that the authentication threshold is greatly improved, an unauthorized user / server is prevented from abusing the authority of the authorized user, the security is higher, the entropy rate of a biological source is not required, and the authentication efficiency is higher.