83 results about "Biometrics" patented technology

The invention relates to the technical field of information security, and discloses a handwriting dynamic encryption and electronic signature method based on a national secret algorithm, and the method comprises the steps: obtaining the handwriting data of a user, and carrying out the preprocessing of the data to remove noise and errors; carrying out feature extraction on the preprocessed handwriting data, extracting corresponding track features, speed features and pressure features, and generating a feature hash value of a unique identifier through a cryptographic SM3 hash algorithm; splicing and combining the feature hash value and a user private key, deriving a dynamic key through a national secret SM2 algorithm, and generating an electronic signature by using the dynamic key; an electronic signature is combined with a timestamp to form a structured signature packet, and the signature packet is hierarchically encrypted and stored in a database through a national cryptographic SM4 algorithm. According to the method and the system, the handwritten signature picture, the digital signature technology, the electronic signature technology and the biological recognition and layout file processing technology are fused, so that the safety and the reliability of electronic document signing are improved.

The invention relates to the technical field of biological recognition and industrial control, and discloses an intelligent console identity verification method based on face recognition, which comprises the following steps: step 1, detecting candidate persons in a preset verification area through a millimeter wave radar; 2, responding to the detected candidate member, starting a near-infrared camera, collecting an eye circumference area image of the candidate member, and synchronously detecting the micro-motion feature of the candidate member by using the millimeter wave radar; and step 3, processing the collected eye circumference area image, performing local identity comparison, performing multi-channel living body detection in combination with the detected micro-motion characteristics, and granting an operation authority when the identity comparison is authorized and the living body detection is passed. A near-infrared camera is combined with a periocular feature comparison scheme, stable recognition under complex illumination and face shielding is achieved, and compared with a traditional visible light recognition scheme, the problem of verification failure caused by hard light, dark light and mask shielding is solved.

The invention discloses a dynamic identity monitoring system based on voiceprint and thermal imaging data fusion, and relates to the technical field of information security and biological recognition, the system can judge whether a user is in an abnormal state such as stress or tension by collecting a user face thermal imaging image and inputting the user face thermal imaging image into a pre-trained deep neural network model; meanwhile, the system extracts voiceprint features of the user, compares the voiceprint features with a pre-registered template, and determines whether the user sounds in real time currently in combination with the matching relationship between the thermal disturbance of the lip region and the sound generation time point; by combining voiceprint recognition and facial thermal imaging analysis, whether the identity of an operator is matched with a voiceprint template or not is verified, whether the operator is a current real sounder or not and whether the operator is in abnormal states such as stress and tension or not is judged in real time, and therefore precise, safe and dynamic control over sensitive operation such as decryption is achieved; and the risks of sound recording pretending and forced decryption are effectively prevented.

The invention provides a user authentication method and device based on a block chain, electronic equipment, a medium and a program product, and can be applied to the technical field of block chain modules and the technical field of biological recognition. The method comprises the following steps: initiating a transaction request in response to a first authentication device, and binding at least one second authentication device based on the transaction request; sending authentication information to the second authentication equipment by using the first authentication equipment, triggering the first authentication equipment and the second authentication equipment to execute authentication operation, and obtaining authentication operation information; performing time-space consistency verification on the first authentication device and the second authentication device based on the authentication operation information; and in response to verification passing, user authentication is completed, a joint signature is generated based on the authentication operation information, and the joint signature and transaction summary information are written into the block chain.

According to one embodiment, a method, computer system, and computer program product for privacy-enhanced, biometrics-based authentication is provided. The embodiment may include capturing credential information on a credential medium and biometric information for a user, each provided by the user, by one or more information capture devices. The embodiment may also include identifying a hash stored on the credential medium based on the captured credential information. The embodiment may further include calculating, locally, a hash of the biometric information using a preconfigured hashing algorithm. The embodiment may also include, in response to the identified hash matching the calculated hash, authenticating the user.

The invention relates to the technical field of server data caching, in particular to a server data caching acceleration system based on edge computing and a control method, and the system comprises a cloud server which is used for storing total to-be-accessed data; the at least one edge server is deployed on a network edge side close to the data access terminal; the at least one data access terminal is used for initiating a data access request to the edge server; the edge server is provided with a cache unit, a prediction unit and a synchronization unit; wherein the cache unit is used for storing a subset of to-be-accessed data prefetched from a cloud server; the prediction unit is used for predicting the future access probability based on the historical access record of the to-be-accessed data; and the synchronization unit is used for synchronizing the prediction result of the prediction unit. According to the method, under the condition that edge storage resources are extremely limited, the core contradiction of biological recognition in the aspects of real-time performance, reliability and efficiency is successfully solved, and a key technical support is provided for intelligent traffic non-inductive passage.

A controller may obtain, from one or more sensors of an operator control of a work machine, sensor data collected from at least one hand of an operator using the operator control. The controller may determine, based on the sensor data, at least one of a physiological profile of the operator or an amount of time that the operator releases the operator control. The controller may cause, based on the at least one of the physiological profile of the operator or the amount of time that the operator releases the operator control, a limitation on a capability of a work implement of the work machine.

The technology provides a multi-body earpiece suitable for use as an in-ear sensor system, which can be used for biometrics or a human-computer interface. The multi-body earpiece includes two body elements connected together by a flexure. These components provide at least 3 points of contact along different parts of the outer ear, in which the flexure is tethered to the two bodies and arranged to lock them in place during wear. In addition to having stability from moving while minimizing sound occlusion, this arrangement enables any electrodes for the on-board sensor(s) to remain in contact with the skin of the ear and provide as many contact points in desired areas as the electronics dictate for the signals of interest.

The invention relates to the technical field of intelligent security and protection, in particular to a key personnel identification passing and early warning system based on multi-modal biological characteristics, and the system is used for executing the flow: collecting and binding to generate a world line object; face features, speaker features and gait features are extracted through alignment modeling, an alignment time event sequence is obtained through optimal transmission, and combined evidence is formed through Copula combined modeling and topological data analysis; according to the consistent anti-verification, geometric and sound source beam consistency, audio-visual mutual prediction anti-verification, visual living body anti-verification and audio playback anti-verification are generated in alignment time, and evidences are accumulated into a logarithmic posteriori trajectory of world line identity; decision control implements confidence control through sequence probability ratio test and conformal prediction, outputs passing, attention or interception instructions, and gives consideration to real-time performance, accuracy and evidence obtaining traceability.

The invention provides a multi-domain electrocardio intelligent analysis method of a mixed Fourier and wavelet convolutional neural network, belongs to the technical field of artificial intelligence and medical health crossing, and solves the technical problems that global spectrum features and local transient features are difficult to consider and task generalization is poor in traditional single-domain ECG analysis. According to the technical scheme, the method comprises the following steps: S1, preprocessing an ECG signal, filtering, segmenting, normalizing and enhancing data; s2, constructing a three-branch model; s3, fusing the attention mechanism with multi-domain features; s4, setting a task classification head; s5, using Adam optimization and an early stop strategy to prevent overfitting; and S6, inputting data, and outputting arrhythmia classification, biological recognition and sleep apnea detection results. According to the method, generalization and accuracy are improved through multi-domain fusion, multiple tasks are supported, and clinical and biological recognition scenes are adapted.

According to the biological recognition method and device, the storage medium and the computer equipment, the hardware parameters and the sample feature sets of the new equipment and the old equipment are collected, and accurate conversion from the old template features to the new equipment features is achieved. The conversion process not only considers hardware differences, but also combines multiple groups of sample features, and ensures that the converted new template features can fully adapt to the hardware environment of new equipment. Meanwhile, local fine tuning is performed on the feature extraction model in the new equipment by using the feature image set of a small number of users and the corresponding new template features, so that the recognition accuracy of the model on the new equipment is further improved. The local fine tuning mode avoids the problems of large data transmission quantity, privacy leakage risk, high operation and maintenance cost, equipment elimination and the like caused by full data synchronization and unified algorithm framework forced upgrading. In addition, the method is also helpful for promoting the popularization and deepening of a biological recognition technology in more fields and scenes.

Embodiments described herein provide for systems and methods for implementing a neural network architecture for spoof detection in audio signals. The neural network architecture contains a layers defining embedding extractors that extract embeddings from input audio signals. Spoofprint embeddings are generated for particular system enrollees to detect attempts to spoof the enrollee's voice. Optionally, voiceprint embeddings are generated for the system enrollees to recognize the enrollee's voice. The voiceprints are extracted using features related to the enrollee's voice. The spoofprints are extracted using features related to features of how the enrollee speaks and other artifacts. The spoofprints facilitate detection of efforts to fool voice biometrics using synthesized speech (e.g., deepfakes) that spoof and emulate the enrollee's voice.

A digital physiological certificate (P-cert) for securing data is disclosed. The certificate comprises a digital file including an identity-based chain of trust of an owner; and a physiological based chain of trust of the owner. The physiological based chain of trust includes an owner's name; an owner's physiological based public key; a publisher's name of the bioauthentication; and a publisher's signature. Other embodiments using the P-Cert are disclosed.

A behavioral biometric deep learning (BBDL) pipeline is provided that includes multiple stages operating to provide machine learning computer models of behavioral biometric authenticators operating on spatiotemporal input data. The BBDL pipeline receives spatiotemporal input data over multiple time intervals, each time interval having a corresponding subset of the spatiotemporal input data. For each time interval, a corresponding stage's machine learning computer model processes the subset of the spatiotemporal input data corresponding to that time interval to generate an output vector having values indicative of an internal representation of spatiotemporal characteristics of an entity. The output vectors are accumulated across the multiple stages of the BBDL pipeline to generate a final output vector indicative of spatiotemporal characteristics of an entity represented in the spatiotemporal input data. The entity is authenticated based on the final output vector.

This invention relates to the field of computer communication technology, specifically to a trusted screen projection method and system based on fingerprint encryption channels. The invention constructs a dynamic "device-person-intent" authentication mechanism through a multi-dimensional root of trust, enhancing the credibility of the projection initiator's identity and the legitimacy of its operation. First, the invention integrates device hardware characteristics and user biometrics to generate a comprehensive trust state. Combining screen content sensitivity and real-time environmental conditions, it dynamically generates encryption keys and environment-bound security payloads to perform differentiated encryption and secure encapsulation of the projected content. This achieves deep coupling and adaptive adjustment of security policies and business content. By continuously updating user biometrics and environmental conditions, it ensures dynamic security protection throughout the entire projection lifecycle, effectively resisting complex security threats, improving the smoothness and response speed of high-definition screen projection, and forming an intrinsic security system with defense-in-depth capabilities.

A digital physiological certificate (P-cert) for securing data is disclosed. The certificate comprises a digital file including an identity-based chain of trust of an owner; and a physiological based chain of trust of the owner. The physiological based chain of trust includes an owner's name; an owner's physiological based public key; a publisher's name of the bioauthentication; and a publisher's signature. Other embodiments using the P-Cert are disclosed.

The invention provides an identity recognition method which can be applied to the technical field of biological recognition. The identity recognition method comprises the following steps: acquiring a plurality of frames of to-be-processed images containing a face region of a to-be-recognized object; performing dynamic feature extraction on the multiple frames of images to be processed to obtain a local optical flow graph representing the facial micro-action of the object to be recognized and a target feature graph representing the semantic representation of any frame of image to be processed in the multiple frames of images to be processed; performing spatial feature extraction on any frame of to-be-processed image in the plurality of frames of to-be-processed images to obtain a facial contour feature map representing the spatial position of the facial region of the to-be-recognized object in the to-be-processed image; performing feature fusion on the local optical flow graph, the target feature graph and the facial contour feature graph to obtain a fused feature graph; and inputting the fused feature map into a pre-training recognition model to obtain an identity recognition result of the to-be-recognized object. The invention further provides an identity device, equipment, a storage medium and a program product.

The utility model provides a light source group, an image receiver, an image acquisition device and electronic equipment, and relates to the technical field of biological recognition, the light source group comprises at least two light sources, each light source is used for emitting polarized light with the polarization degree not equal to 0, and the polarized light emitted by the light source group has at least two polarization directions; the polarized light in at least two polarization directions is reflected by the target object and then carries polarization information corresponding to the contour surface of the target object to sequentially enter the image sensor. The polarized light emitted by each light source in the light source group is optimized, so that the structure of the light source group is simplified, when the light source group is applied to three-dimensional imaging, target object recognition or anti-counterfeiting recognition and other schemes, the schemes can be simplified, and accurate face contour reconstruction and recognition precision improvement are realized with a relatively simple scheme and relatively low cost.

The technology provides a multi-body earpiece suitable for use as an in-ear sensor system, which can be used for biometrics or a human-computer interface. The multi-body earpiece includes two body elements connected together by a flexure. These components provide at least 3 points of contact along different parts of the outer ear, in which the flexure is tethered to the two bodies and arranged to lock them in place during wear. In addition to having stability from moving while minimizing sound occlusion, this arrangement enables any electrodes for the on-board sensor(s) to remain in contact with the skin of the ear and provide as many contact points in desired areas as the electronics dictate for the signals of interest.

The present invention relates to a system and a method for multi-factor authentication of communication device metadata. The system includes a communication device, and a recipient device that work together to authenticate communication device metadata using digital signatures and a user's identity through voice biometrics. User enrolls into the system by providing voice samples, from which a unique voiceprint is created and stored. For authenticating the communication device metadata with user authentication, the user issues a voice command, and the system extracts voice signatures which are compared with the stored voiceprint to verify the user's identity. Upon successful authentication, the communication device metadata is accessed, and corresponding digital signatures are generated. Further, the system authenticates the generated digital signatures and user's identity. If both are verified, the communication device metadata is considered valid and used for further processing.

The invention discloses a decryption operation identity verification system based on cooperation of voiceprint and radar data, and relates to the technical field of information security and biological recognition. The system extracts voice features of an operator through a voiceprint acquisition module and compares the voice features with a pre-registered template to complete identity verification; meanwhile, the opening and closing actions of the mouth of the operator are obtained through the radar acquisition module, and the sounder is spatially positioned. And the main target recognition module correspondingly matches the voiceprint result with the radar spatial data, and accurately calibrates an operator who makes a sound actually. And after the verification is passed, the system continuously tracks key point tracks of the main operator and other personnel in a decryption window period, and a cooperative behavior detection module further analyzes whether cooperative intervention behaviors such as synchronization and approaching exist between the main operator and the non-main operator, and outputs an alarm when an abnormal structure is identified. The problem that an operator may be forced to execute decryption operation in the identity verification process is solved.

The application is suitable for the field of biometric identification, and provides a living body detection model training method, a living body detection method, and a living body detection model training method, which comprises the following steps: obtaining an initial lightweight model and a teacher model; obtaining a training sample set, wherein the training sample set comprises face sample data; inputting the face sample data into the teacher model for processing to obtain first dimension information; inputting the face sample data into the initial lightweight model and training based on the first dimension information to obtain a target living body detection model. According to the above scheme, the living body detection model obtained through training is more lightweight, and the model recognition speed and accuracy are greatly improved.

The present invention relates to an electric vehicle (EV) charging system. The electric vehicle (EV) charging system includes an electric vehicle charger, a security monitoring module, a fleet management module, and a charging management platform. The electric vehicle charger is configured to supply power to a connected vehicle and a sensor interface unit (SIU) that enables user authentication through multi-modal biometrics such as voice, facial, fingerprint, RFID / NFC, license plate, and OTP verification, with a prioritised voice-based fallback. The security monitoring module continuously detects cable tampering, power anomalies, and unauthorised access, triggering real-time alerts. Further, a generative AI module analyses authentication, sensor, and network data to identify threats, correlate cyber and physical events, and generate adaptive responses. The fleet management module facilitates multi-level control with role-based authentication for super administrators, managers, and drivers. The charging management platform is provided for session validation, billing, and data recording.

The application is suitable for the field of biometric identification technology, and provides a gait and facial feature fusion identification method and system of deep learning architecture, the method comprises the following steps: monitoring the card swiping event of the subway gate, analyzing the card ID information and generating a dynamic time window signal; collecting the plantar pressure distribution and facial video of the card swippers and processing to obtain the spatio-temporal alignment foot pressure time series data and the anti-occlusion facial image; reconstructing the continuous gait phase feature according to the foot pressure time series data, inversely restoring the three-dimensional facial biometric feature under the extrusion state by using the anti-occlusion facial image, and integrating to obtain the human biometric feature vector; performing confidence evaluation on the human biometric feature vector and performing dynamic weighted fusion to obtain the multi-modal feature, and then generating the final identity authentication result through the multi-round voting mechanism within the time window. The application realizes high-precision and low-misrecognition-rate fusion identification in a complex scene, and provides a stable and reliable solution for the intelligent management of the subway gate.

The cattle facial recognition method and system belong to the field of biometrics. It solves the problems of existing cattle recognition methods, such as high false recognition risk, low recognition efficiency, high cost, and specific requirements for recognition equipment and environment, making them unsuitable for modern large-scale farms. The method includes the following steps: Step S1, acquiring images and preprocessing them to establish an image dataset, which includes a facial feature dataset and its corresponding identity information dataset; Step S2, performing target detection on the facial feature dataset based on the YOLOv7 model; Step S3, recognizing the detected facial feature dataset using the FaceNet facial recognition method, thus completing the recognition of the facial feature dataset and simultaneously displaying the corresponding identity information dataset.

Embodiments include systems and methods for user authentication using behavioral biometrics. In some embodiments, the method includes collecting raw behavioral data regarding user interactions with a device; extracting a plurality of features from the raw behavioral data; mapping the features to a plurality of mathematical spaces; generating, based on the mapped features, a unified behavioral profile; and determining, based on the unified behavioral profile, an authentication decision for a user of the device.

Embodiments described herein provide for a voice biometrics system execute machine-learning architectures capable of passive, active, continuous, or static operations, or a combination thereof. Systems passively and / or continuously, in some cases in addition to actively and / or statically, enrolling speakers as the speakers speak into or around an edge device (e.g., car, television, radio, phone). The system identifies users on the fly without requiring a new speaker to mirror prompted utterances for reconfiguring operations. The system manages speaker profiles as speakers provide utterances to the system. Machine-learning architectures implement a passive and continuous voice biometrics system, possibly without knowledge of speaker identities. The system creates identities in an unsupervised manner, sometimes passively enrolling and recognizing known or unknown speakers. The system offers personalization and security across a wide range of applications, including media content for over-the-top services and IoT devices (e.g., personal assistants, vehicles), and call centers.

Disclosed herein is a method for facilitating self-optimizable biometrics identity verification of users, in accordance with some embodiments. Accordingly, the method includes obtaining one or more verifying data. The method includes retrieving one or more first verifying data of the user based on the verification requirement. The method includes matching the one or more verifying data with the one or more first verifying data of the user. The method includes determining an accuracy of a match between the one or more verifying data and the one or more first verifying data. The method includes generating a result for the self-optimizable biometrics identity verification based on the accuracy of the match. The method includes transmitting the result to a device. The method includes a step of authorizing the user for an interaction based on the result.

The Symbolic Social Sovereignty Protocol (S3P) discloses a decentralized, cryptographically sovereign social media infrastructure that supersedes legacy centralized platforms through symbolic identity binding, consent-enforced data flow, and algorithmic transparency. The system replaces algorithmic manipulation, data exploitation, and ad-driven manipulation with a symbolic framework of social cognition integrity, modular content governance, and real-time user-controlled propagation permissions. All user interactions are identity-authenticated using zero-knowledge symbolic biometrics, cryptographically signed consent logs, and treaty-based algorithmic overlays ensuring compliance with local jurisdiction, age, and psychological safety. The protocol introduces a modular post propagation pipeline with embedded social contracts, memory integrity chains, consent-token threading, and symbolic logic graphs replacing opaque feed ranking. A decentralized neural moderation mesh, treaty-based trust weighting, and composable symbolic governance are deployed across edge, mesh, and sovereign cloud environments. Designed to comply with emerging 2025 global AI, data, and child protection laws, S3P delivers an interoperable, fraud-resistant, emotionally safe, and sovereign communication fabric.