34 results about "Biometric fusion" patented technology

The invention discloses an automatic fare collection method and system based on multi-modal identity recognition, and relates to the technical field of rail transit automatic fare collection systems and multi-modal biological recognition fusion, and the method comprises the following steps: 1, enabling a user to approach a recognition area, carrying out the multi-modal data collection, and carrying out the quality detection and preprocessing; 2, feature extraction and credibility preliminary calculation are carried out; 3, environment data are collected and subjected to standardization processing; step 4, adaptively predicting a weight through machine learning, and constructing weight smoothing and time domain constraints; step 5, carrying out D-S evidence step-by-step fusion, and calculating comprehensive credibility; and step 6, comparing the comprehensive credibility with a preset threshold value, if the comprehensive credibility is greater than or equal to the preset threshold value, determining that the identification is successful and the gate is opened for passing, otherwise, determining that the credibility is insufficient, and performing alternative / manual processing. The system comprises a front-end identification terminal, a read-write industrial control all-in-one machine and a cloud management platform.

The invention relates to the technical field of artificial intelligence, in particular to a driver fatigue state monitoring system and method based on multi-modal biological feature fusion, and the system comprises a sensing module, a data processing and analysis module, an environment risk assessment module, a personalized model module, a self-adaptive decision fusion module, and a vehicle interaction and vehicle control module. Compared with the prior art in which fatigue judgment is performed by adopting a fixed threshold value, the fatigue judgment cannot adapt to a complicated and changeable driving environment and may cause excessive alarm or missing alarm under a monotonous road condition in a dangerous road section, an environment perception and dynamic threshold value adjustment algorithm is introduced, so that the system can intelligently evaluate the environmental risk and monotonicity, and the fatigue judgment accuracy is improved. The alarm sensitivity is automatically adjusted, and the early warning accuracy and the scene adaptability are both improved.

The invention provides an intelligent bullet cabinet control management system and method, relates to the field of bullet cabinet management, and adopts a data analysis technology based on artificial intelligence to judge whether a user who wants to open a bullet cabinet is an authorized user of the bullet cabinet or not by performing fingerprint and iris analysis on the user who wants to open the bullet cabinet. And opening the cartridge cabinet in response to the user being the cartridge cabinet authorized user. Therefore, through a multi-biological-feature fusion mode, the reliability of identity verification of the gun and ammunition cabinet is greatly improved, so that the safety management of guns and ammunitions is more effectively realized.

A system for the real-time delivery of personalized motivational content based on biometric information; the system includes: a biometric acquisition module configured to capture a variety of physiological signals from a user, wherein the physiological signals include at least heart rate variability, electrodermal activity, facial expressions and electroencephalographic (EEG) signals; a preprocessing module that is operationally coupled with the biometric acquisition module, wherein the preprocessing module is configured to remove noise, normalize and extract signal features from the physiological signals in real time; a multimodal biometric fusion engine configured to temporally align and synchronize the extracted features across signal modalities using dynamic time distortion and confidence-weighted interpolation; a motivational state inference model with a hybrid neural architecture comprising a Convolutional Neural Network (CNN) for spatial pattern recognition and a Recurrent Neural Network (RNN) for temporal sequence modeling, wherein the inference model is configured to output a motivational input score and an affective state classification; an engine for recommending motivational content, configured to select and prioritize content from a content repository based on motivational uptake score, user profile metadata, contextual signals including time of day and geolocation, and historical content effectiveness profiles; and a content delivery subsystem comprising one or more output modalities selected from an acoustic actuator, a visual display, a haptic actuator or an environmental controller, wherein the content delivery subsystem is capable of presenting the selected motivational content in a modality that is dynamically adapted to the user's current psychophysiological state.

The invention discloses a real-name DID and biological feature fusion rapid verification system, and the system comprises the following modules: a biological feature processing module which is used for generating a nested biological feature vector; the hash generation module is used for performing hash operation and constructing a Merkle tree; the real-name verification module is used for generating real-name identity data; the DID construction module is used for generating a DID document and adding a BioBinding extension field; the proof generation module is used for generating a verification record; the block chain writing module is used for forming an on-chain real-name DID record; the verification module is used for matching with the on-chain real-name DID record; the confidence control module is used for comparing the matching result with a preset verification threshold value, and if verification is passed, authorized access is completed; and if the verification is not passed, triggering a multi-factor re-verification mechanism to perform secondary verification, calculating a DID confidence value, and if the secondary verification is still not passed, executing identity freezing. According to the method, multi-modal biological recognition and a real-name DID generation mechanism are fused, and on-chain credible verification is realized.

The invention provides a topology and biological feature fusion-based virulence gene prediction method, which comprises the following steps of: obtaining a to-be-detected gene; inputting the to-be-detected gene into a trained DAVGAE model, and predicting the correlation degree of the to-be-detected gene and the disease to obtain a disease gene correlation prediction conclusion; wherein the DAVGAE model comprises a data enhancement module, an encoder and an inner product decoder. The problems of data sparsity and heterogeneous data integration in gene-disease association prediction can be effectively solved at least through a DAVGAE model formed by a data enhancement module, an encoder and an inner product decoder.

The invention discloses a storage method and system for an intelligent storage cabinet, and the method comprises the steps: receiving authority parameters of an online examination and approval request and user biological characteristics, and generating an encrypted digital token; based on the encrypted digital token, performing identity authentication by adopting a multi-mode biological feature fusion algorithm, associating face features with voiceprint features through a space-time attention mechanism, and outputting a dynamic identity verification code; activating a target storage cabinet group according to the dynamic identity verification code, and generating an article unique identifier set; based on difference analysis of the unique article identifier set and a preset inventory list, outputting an optimal cabinet grid allocation scheme; and driving a modular cabinet group execution mechanism to adjust a physical structure according to the optimal cabinet grid distribution scheme, synchronously encrypting and writing article state data into a distributed account book, and triggering an edge computing node to update a global inventory topological graph. According to the embodiment of the invention, intelligent, self-adaptive and distributed collaborative management of the storage cabinet can be realized on the premise of ensuring the safety.

The invention discloses an individual health risk dynamic assessment system based on multi-modal biological feature fusion, and relates to the technical field of medical health information processing. The method comprises the following steps: constructing a high / low-dimension data combination rule; dynamically adjusting the feature fusion weight according to the clinical feature importance weight and the medical efficiency value calculated by the detection time interval; a cost difference value formula is introduced to quantify the economical efficiency of combined evaluation and independent evaluation; calculating a health risk comprehensive coefficient of the monitoring scene by combining the health data credibility score and the index type / amplitude / duration weighted risk value; the risk level is rechecked through expert assessment or a secondary model, so that the assessment accuracy is ensured; updating the individual credibility score according to the evaluation result; and for the result exceeding the confidence interval, classifying and optimizing a feature fusion algorithm, a model parameter or a data combination strategy. The system realizes accurate, economic and real-time individual health risk dynamic assessment, and is suitable for scenes of chronic disease management, health intervention and the like.

The invention discloses an analysis method and system for AI recognition of physical exercise trajectory data. The method comprises the following steps: step 1, constructing a space-time-environment-biological feature fusion network, integrating collected multi-source data, capturing association of trajectory coordinates, skeleton joint angles and electromyographic signals, constructing a space-time dynamic graph, and comprehensively sensing the influence of a dynamic environment on a motion trajectory; 2, introducing a generative adversarial network to simulate extreme environment interference, and improving model robustness; forcing the model to learn a defensive decision through physical layer disturbance and behavior layer disturbance; distinguishing a real track from a generated track by using a discriminator, and trying to cheat the discriminator by a generator to enable the model to adapt to a noise environment; 3, cross-athlete skill migration is conducted, a parameter freezing strategy and dynamic weight adjustment are adopted, the basic exercise mode recognition capacity is reserved, and meanwhile individual differences are adapted; and step 4, in combination with deep reinforcement learning and an interpretable rule engine, carrying out instant tactical adjustment, and generating an action through a DRL strategy network.

The invention provides a multi-modal biological feature fusion-based identity recognition model construction method, a multi-modal biological feature fusion-based identity recognition model use method, a multi-modal biological feature fusion-based identity recognition model system, a storage medium and a program product, and relates to the technical field of identity recognition. According to the method, biological feature original data of at least two different modalities are obtained, feature vectors are extracted, the dimensionality and richness of identity recognition information are increased, then the feature data of different modalities are embedded into dimension spaces with the same dimensionality, the feature data of different modalities are made to have comparability, and the accuracy of identity recognition is improved. According to the method and the system, subsequent generation of fusion features and training of an identity recognition model are facilitated, through mutual complementation of multiple modal features, the difficulty of counterfeiting by an attacker is increased, the probability of success of counterfeiting attacks is reduced, the security of identity recognition is improved, and the problem that the success rate of counterfeiting attacks is high when only single-modal biological features are depended on is solved. Meanwhile, an identity recognition model is trained by adopting a federal learning method, and the data is locally processed, so that the security risk caused by original data transmission is reduced.

The present application provides a real-time monitoring method for driver fatigue state based on multimodal biometric fusion, which relates to the technical field of fatigue alarm. The method includes: deploying monitoring components to sample the driver's posture; embedding and deploying a first decision-making unit and a second decision-making unit; connecting a fatigue alarm, and according to continuous monitoring frames, coordinating the first decision-making unit and the second decision-making unit to give a status reminder to the driver. Among them, the first decision-making unit makes a spatio-temporal dimension decision through the Euler angles of the head posture, and the second decision-making unit makes a spatio-temporal dimension decision through the correction based on the Euler angles of the head posture and the multimodal biometric fusion determined based on the aiming frame vector. By means of the present application, the technical problem that the monitoring accuracy is low and false alarms and missed alarms are likely to occur due to relying on a single facial feature and not considering individual characteristics is solved. Through multimodal biometric fusion and two-level decision-making units, the accuracy and efficiency of fatigue early warning are improved.

The invention provides a continuous authentication method based on behavior feature and biological feature fusion, which comprises the following steps: acquiring behavior sequence data by monitoring a user interaction log in real time, and extracting a timestamp labeled action mode and environment variable combination from the behavior sequence data to obtain a transition signal identifier; for a bridging interval, processing a tail end mode and an initial starting habit of behavior sequence data through a transition detection algorithm, and fusing a historical behavior template to generate a smooth feature vector; after the smooth feature vectors are obtained, the biological recognition data are integrated through a feature fusion algorithm, for example, a fingerprint scanning result and behavior sequence data are subjected to weighted merging to obtain a comprehensive authentication score; if the comprehensive authentication score is higher than a preset threshold value, determining that the authentication strength is maintained at the current level, and adjusting the subsequent acquisition frequency by updating a dynamic model of the behavior sequence data; and acquiring a new round of behavior sequence data from the user interaction log according to the adjusted acquisition frequency, and repeating the transition signal identifier extraction process to monitor potential discontinuity.

The invention discloses a wind power plant face recognition safety management system and method, and the system comprises a data obtaining module which is used for obtaining wind power plant environment data and personnel biological characteristic data; the face recognition module is used for performing face recognition and dynamically adjusting the user permission based on the wind power plant environment data and the personnel biological characteristic data; the face recognition module comprises a self-adaptive multi-mode biological feature fusion system, an ultra-high-speed face matching engine based on quantum computing, a situation awareness dynamic authority management system and a homomorphic encryption face recognition module; personnel biological characteristic data are fused through a self-adaptive multi-mode biological characteristic fusion system, a super-high-speed face matching engine based on quantum computing is used for recognizing a face, and meanwhile, based on wind power plant environment data, a dynamic authority management system based on context awareness is used for dynamically adjusting the authority level; the homomorphic encryption face recognition module is adopted to encrypt the face recognition process, and face recognition can be achieved while personal privacy is protected.

The application discloses a real-name DID and biological feature fusion rapid verification system, comprising the following modules: a biological feature processing module for generating a nested biological feature vector; a hash generation module for performing hash operation and constructing a Merkle tree; a real-name verification module for generating real-name identity data; a DID construction module for generating a DID document and adding a BioBinding extension field; a proof generation module for generating a verification record; a blockchain writing module for forming a chain real-name DID record; a verification module for matching with the chain real-name DID record; a confidence control module for comparing the matching result with a preset verification threshold, if the verification is passed, authorized access is completed; if the verification is not passed, a multi-factor re-verification mechanism is triggered for secondary verification, a DID confidence value is calculated, if the secondary verification is still not passed, identity freezing is executed. The application fuses multi-modal biological recognition and real-name DID generation mechanism, and realizes chain trusted verification.

The invention discloses a palm biological feature fusion recognition method and device, a storage medium and equipment, and belongs to the field of biological feature recognition. According to the method, the characteristic that the distribution positions of the palm print main line and the palm vein blood vessels are relatively fixed is utilized, the intersection points of the palm print main line and the palm vein blood vessels serve as the feature points, the feature point set used for registration and recognition is constructed, feature-level fusion of the palm prints and the palm veins is achieved, and comparison is conducted through the fused feature point set, so that the registration accuracy is improved. Therefore, palm biological feature fusion recognition is realized. According to the method, the advantages that the palmprint principal line for palmprint recognition is thicker, obvious and stable in feature and not prone to interference compared with other features, the blood vessel near-infrared imaging for palmprint vein recognition is hidden and not prone to interference, and the advantages of correlation and dependence of relative position distribution of palmprint and palmprint vein are combined, so that the accuracy of palmprint and palmprint vein recognition is improved, and the accuracy of palmprint and palmprint vein recognition is improved. The recognition precision and the recognition reliability of biological characteristics are greatly improved, and accurate recognition of palm prints and palm veins is achieved.

The application relates to the field of payment security technology, in particular to a commodity supply chain platform payment security authentication method based on biological feature authentication. The method obtains biological feature vectors of a sending party and a receiving party as reference vectors; according to the size and fluctuation of elements at the same position in the reference vectors of each party, reference average vectors and the confidence degree of each position element in the reference average vectors are obtained; the specified position of the elements that need to be reserved in the reference average vectors is determined based on the confidence degree; the biological feature fusion vector is obtained according to the elements at the specified position after binaryzation, and then the real-time encryption key and the real-time decryption key are obtained, and the commodity supply chain platform is subjected to payment security authentication. The biological feature fusion vector is obtained, the stability of the key is effectively improved, the leakage of the key in the transmission process is avoided, and the accuracy and efficiency of the payment security authentication of the commodity supply chain platform are effectively improved.

A system and method for biometric-authenticated personal health monitor data compaction with clinical trial optimization is disclosed. The system receives biometric signals from multiple sensor modalities associated with a patient and extracts distinctive biometric features using signal processing algorithms. Patient identity verification is performed by comparing extracted features against stored biometric templates, generating cryptographic keys derived from verified biometric characteristics. Health data is divided into sourceblocks and encoded using multiple compression codebooks enhanced with biometric-derived cryptographic keys. Optimal encoded sourceblocks are selected based on compression efficiency and statistical preservation requirements. A clinical trial data optimization engine classifies health data by type and endpoint significance, determines statistical preservation requirements for regulatory compliance, and validates that compressed data maintains required statistical properties for clinical analysis. The system implements multi-modal biometric fusion, liveness detection, emergency override capabilities, and security controls including role-based access control and audit logging for secure clinical trial data management.

Provided is a method for recognizing an individual by fusing a first match result generated by a first single-modal biometric recognition system of a first biometric modality and a second match result generated by at least one second single-modal biometric recognition system of a second biometric modality into a fused match result. Machine leaning based on training of retrieved data is performed to obtain optimized parameters meeting a target performance metric. Parameters of a fusion model are updated with the optimized parameters in a database, which is then applied to match results to recognize the individual. Other embodiments disclosed.

The invention discloses a security authentication method and system based on multi-modal biological feature fusion, and relates to the technical field of biological feature recognition. Environmental factor influence performance index data sets are constructed and grouped by using a clustering algorithm; determining an environmental condition classification set containing groups with insufficient light and normal noise, analyzing a change trend by adopting a sliding window method aiming at performance fluctuation under the conditions of insufficient light, noise interference and the like, generating a core adaptation rule set, and verifying corresponding adaptation adjustment parameters through a simulation environment to obtain a core adaptation rule set; finally, a biological recognition authentication output rule set is formed, a comprehensive analysis framework is constructed, various expression data in the authentication process are systematically monitored and combed, a performance change rule is found through the data, the authentication stability and accuracy in a complex environment are ensured, and the authentication efficiency is improved. And the adaptability and the reliability of a biological recognition technology are remarkably improved.

This invention discloses a multimodal biometric fusion-based security screening system to prevent missed checks, relating to the fields of artificial intelligence and civil aviation information technology. It includes a risk perception unit, a data acquisition unit, a real-time monitoring unit, a data analysis unit, and a verification unit. This invention proactively identifies and marks high-risk targets in the isolation area through intelligent behavior analysis, enabling precise triage and enhanced verification of passengers entering the isolation area to ensure key targets are under control. Finally, leveraging multimodal biometric fusion technology, continuous identity verification and dynamic tracking are performed at key nodes such as security checkpoints and jet bridges. This closed-loop process not only achieves a shift from passive prevention to proactive intervention but also significantly improves the real-time detection capability and precise interception efficiency of missed checks through a layered prevention and control mechanism, effectively preventing passengers from entering the isolation area or even boarding the plane without security checks, thus comprehensively enhancing the reliability and overall operational efficiency of the security screening system.

The present invention relates to the field of key technology, and more specifically, to a multimodal biometric fusion system based on one-time key protection. It includes a revocable biometric conversion unit, a key guide fusion unit, a one-time protection unit, a key recovery unit, and a dual authentication unit. The present invention enriches the diversity of template fusion schemes on the one hand, and increases the difficulty for attackers to attack the system on the other hand. The generation of fusion templates is related to keys, and the keys are distributed and stored in different databases in a shared form. The keys can be restored losslessly only when all the shares are obtained, so as to generate hybrid templates. Both biometrics and keys are indispensable, and jointly ensure the security of the system and the privacy of users, and will not be forgotten and are not easy to be forged.

The present invention provides a multi-biometric feature fusion recognition system and device, belonging to the field of intelligent recognition technology, including: a matrix construction module, used to extract features of each type of biological information according to biological verification points, and construct the user's initial feature matrix; a matching analysis module, used to extract a number of elements from each row vector in the initial feature matrix and perform normalization processing to construct a normalization function; an instruction issuing module, used to issue an unlocking instruction to the vehicle when the normalization function matches the set function required for vehicle unlocking; otherwise, the column information of each column vector in the initial feature matrix is ​​matched with the pre-stored information to obtain the matching coefficient, and the biological image is processed to obtain the unlocking reliability; a reliability determination module, used to pre-process all matching coefficients and unlocking reliability to obtain comprehensive reliability. Effectively improve the recognition reliability and the safety of vehicle door lock control, and reduce the risk of vehicles being used indiscriminately.

The invention provides a multi-modal biological feature fusion-based driver fatigue state real-time monitoring method, and relates to the technical field of fatigue alarm, and the method comprises the steps: deploying a monitoring assembly, and carrying out the sampling of a driver posture; deploying a first decision maker and a second decision maker in an embedded manner; the state reminding module is connected with the fatigue alarm and cooperates with the first decision-making device and the second decision-making device according to the continuous monitoring frames to remind the driver of the state; wherein the first decision-making device carries out space-time dimension decision-making through a head posture Euler angle, and the second decision-making device carries out space-time dimension decision-making through fusion of correction based on the head posture Euler angle and multi-mode biological characteristics determined based on aiming frame vectors. According to the invention, the technical problems of low monitoring precision and easy occurrence of false alarm and missing alarm caused by dependence on single facial features and no consideration of individual characteristics are solved, and the accuracy and efficiency of fatigue early warning are improved through multi-modal biological feature fusion and a two-stage decision maker.

The present invention discloses a method for hierarchical and decentralized management of enterprise data based on a large language model (RAG), which relates to the technical field of data security and access control. The method comprises the following steps: obtaining a voice audio stream and a lip shape three-dimensional trajectory point set, loading permission baseline parameters, and generating a permission token, wherein the permission token comprises a quantum watermark seed, a role identifier, a permission token expiration stamp, and a permission level; obtaining permission data blocks through a retrieval enhancement generation framework based on the permission level and role identifier, dividing the hierarchies according to the permission baseline parameters, generating retrieval keywords based on the permission token, establishing a hierarchical index, matching permission data blocks, reordering the permission data blocks after matching the role identifiers, generating a response text, converting the quantum watermark seed into a binary stream, and embedding the stream into the response text to obtain a watermark response text; the present invention solves the problem of lack of dynamic risk perception in permission decision-making through multimodal biometric fusion and Shannon entropy value calculation.

The invention relates to the technical field of payment security, in particular to a commodity supply chain platform payment security authentication method based on biological feature authentication. The method comprises the following steps: acquiring biological feature vectors of a sender and a receiver as reference vectors; obtaining a reference average vector and the confidence degree of each position element in the reference average vector according to the size and fluctuation of the same position element in the reference vector of each party; determining a specified position of each element needing to be reserved in the reference average vector based on the confidence degree; and obtaining a biological feature fusion vector according to a vector constructed after binaryzation of the elements on the specified position, thereby obtaining a real-time encryption key and a real-time decryption key, and performing payment security authentication on the commodity supply chain platform. According to the invention, by obtaining the biological feature fusion vector, the stability of the secret key is effectively improved, the leakage of the secret key in the transmission process is avoided, and the accuracy and efficiency of payment security authentication on the commodity supply chain platform are effectively improved.

The invention relates to the technical field of financial data processing, in particular to an online payment transaction system and a transaction method thereof. The method comprises the following steps: obtaining user transaction data based on a payment platform; performing user image acquisition processing through a camera preset on the POS machine to obtain a user face image; the method comprises the following steps: acquiring user fingerprint data through a preset fingerprint acquisition area on a POS machine to obtain the user fingerprint data; performing seamless identity verification processing on the user face image and the user fingerprint data to generate user identity identification data; performing credit card generation processing on the user identity identification data by using a dynamic biological feature fusion algorithm to obtain a user dynamic credit card; according to the POS machine card swiping system and the card swiping transaction method thereof, the user information is subjected to multi-mode verification, and the virtual credit card is constructed to carry out risk assessment on the user, so that the POS machine card swiping system and the card swiping transaction method thereof are realized.

The application relates to the technical field of ticket selling equipment, and discloses a rail transit non-inductive ticket checking method and system based on space-time trajectory and biological feature fusion, which comprises the following steps: continuously acquiring the space coordinate sequence of a moving target in a ticket checking area and corresponding biological recognition feature groups; determining the distribution weight according to the running deflection angle of the moving target relative to a sensor, calculating a verification confidence index; establishing a ticket checking decision line, a first buffer zone and a second buffer zone with different physical lengths on both sides of the ticket checking decision line on a passing path; monitoring the displacement vector of the moving target relative to a decision domain, and driving a ticket checking transaction state machine to migrate; and the application eliminates the mapping ambiguity between identity evidence dispersion and physical displacement continuity through an asymmetric logic buffer zone and a state machine migration mechanism.

The present invention discloses a biometric-based pig individual identification and health monitoring system, which relates to the field of medical technology. The system includes a multimodal biometric acquisition module for acquiring a pig's 3D facial features, ear vascular distribution characteristics, gait dynamic characteristics, and physiological data; a biometric fusion recognition module that fuses multimodal features based on a Transformer architecture to achieve individual uniqueness identification and dynamic updating of feature templates; and a digital twin health monitoring module that constructs a digital twin model of the pig's entire life cycle and combines it with an LSTM neural network to achieve health status assessment and early disease warning. This biometric-based pig individual identification and health monitoring system uses multiple devices, including an RGB-D camera, a near-infrared camera, and a binocular vision sensor, to construct a 1024-dimensional standardized feature vector. Combined with a Transformer network to dynamically assign modal weights, it achieves high-precision recognition in complex scenarios.

This invention discloses a method and system for vehicle driver identification based on multi-source data fusion, belonging to the field of intelligent driving and identity authentication technology. The method includes: simultaneously acquiring the driver's facial image, iris image, voice data, and driving behavior data through a multi-source perception layer; performing liveness detection on the facial and iris images; if the liveness detection is successful, performing parallel feature extraction on the multi-source biometric data; dynamically adjusting the weights of each feature modality based on environmental context information; performing a fusion decision on each feature modality based on the weights to obtain a confidence score for identity verification; comparing the confidence score with a preset threshold; if the confidence score is greater than or equal to the preset threshold, determining that identity verification is successful, and binding the driver's identity identifier to the security chip of the security execution layer; and uploading the hash value of the identity verification event to the blockchain for storage. This invention employs a multi-source biometric fusion strategy, combined with environmental context perception and a dynamic weight allocation mechanism, effectively improving the accuracy and robustness of identity recognition, enhancing the system's security and anti-spoofing capabilities, and achieving tamper-proof storage of identity verification events through blockchain technology, making it suitable for identity authentication scenarios in intelligent connected vehicles.