11 results about "Biometric fusion" patented technology

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 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 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.

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.

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.

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.

The invention 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, and the method comprises the steps: obtaining a continuous space coordinate sequence of a moving target in a ticket checking region and a corresponding biological recognition feature group in real time; determining an allocation weight according to the advancing deflection angle of the moving target relative to the sensor, and calculating a verification confidence index; establishing a first buffer area and a second buffer area on the passage path, wherein the first buffer area and the second buffer area comprise a ticket checking decision line, are distributed on two sides of the ticket checking decision line and have different physical lengths; and monitoring a displacement vector of a moving target relative to a decision domain, driving a ticket checking transaction state machine to migrate, and eliminating mapping fuzziness between identity card discretization and physical displacement continuity through an asymmetric logic buffer area and a state machine migration mechanism.

The invention relates to the technical field of intelligent lighting control and human factor engineering, in particular to a human factor lighting self-adaptive adjusting method based on multi-biological-feature fusion and dynamic learning, and the method comprises the steps: synchronously collecting multi-source biological features of a user, such as electroencephalogram, eye movement, heart rate variability and facial thermal imaging; accurate user state estimation is generated through dynamic weighting of a fusion network based on an attention mechanism. On the basis, the system maintains and continuously updates a personal illumination response model library for each user, and online exploration optimization is carried out while a historical optimal illumination strategy is utilized in combination with a reinforcement learning framework, so that highly personalized illumination adjustment parameters are generated. After adjustment, the model is continuously improved through closed-loop verification, and rapid starting and long-term rhythm adaptation of a new user are achieved through meta-learning. According to the invention, the problems of single perception and lack of individuation and self-evolution ability in the prior art are solved, and intelligent light environment adjustment with comprehensive perception, continuous optimization and deep fitting of a human body is realized.