2039results about "Multiple biometrics use" patented technology

The invention provides a method and system for preventing identity spoofing during digital authentication processes using artificial intelligence (AI)-driven pattern recognition. The system receives an input data stream from a user attempting to authenticate, which may include biometric data, device behavior data, or user interaction data. An AI-based pattern recognition model processes this data to analyze user behavior patterns and detect any anomalies that may indicate potential spoofing attempts. The system compares the processed data against a pre-established user profile to generate an authentication decision. If anomalies are detected, the system can flag the authentication for further review or trigger additional verification steps, such as multi-factor authentication (MFA) or one-time password (OTP) prompts. The system continuously learns from user interaction data and dynamically updates the user profile to improve the accuracy of identity verification.

The invention provides a private domain live broadcast data storage and visitor authentication method and system based on a block chain, and relates to the technical field of data storage and access control, and the method comprises the steps: collecting multi-mode biological characteristic data of a visitor, carrying out the feature extraction and fusion through a deep neural network of a space-time attention mechanism, and carrying out the authentication of the visitor; authenticity verification is carried out in combination with an adversarial generative network, a dynamic identity token is generated, and a hash value of the dynamic identity token is written into the block chain. The method comprises the following steps: performing semantic analysis on live broadcast data flow through a self-adaptive content perception model, constructing a double-layer access control matrix according to a data sensitivity level and a credibility score, performing self-adaptive fragmentation and hierarchical encryption based on federated learning, and then storing. After an access request is subjected to identity verification, an optimal edge node is selected according to access authority and network performance parameters to construct multi-path transmission, and a transmission strategy is generated based on a service quality score and a data sensitivity level. And when the service quality is lower than a threshold value, triggering the smart contract to perform fault migration.

A system for serving personalized advertisements based on biometric and identifying data captured by sensors. The system employs pre-processing, feature extraction, and recognition modules to identify individuals. A context-aware processing module adapts recognition strategies based on environmental data and analyzes personal information to confirm the identity of the targeted individual and send targeted advertisements. The system includes display and speaker modules for visual and audio advertisements, a predictive interaction modeling module for generating personalized advertisements, and a cross-platform behavior analysis module for analyzing user behavior across offline and online platforms.

The invention provides an intelligent classroom teaching optimization method and system combining behavior recognition and the Internet of Things, and relates to the technical field of smart classs.The method comprises the steps that student behavior data, audio and video data and environment data are acquired through a distributed Internet of Things data acquisition system deployed at a student terminal; processing the data by adopting a multi-modal feature fusion network to obtain student interaction state features; and constructing a remote teaching interaction evaluation model based on the features, calculating the attention level and the classroom participation degree of the students in real time, and generating a personalized teaching guidance strategy. According to the method, intelligent sensing of student states and dynamic optimization of teaching strategies in the remote teaching process are realized.

The invention provides a lightweight digital human lesson preparation system based on an intelligent agent, and belongs to the field of intelligent teaching. Through collaborative operation of four core modules of knowledge graph construction and reasoning, multi-modal cognitive agent, lightweight digital human generation and intelligent teaching plan assistance, the problems of low efficiency of resource integration, teaching content homogenization, insufficient digital human interaction experience and the like in traditional lesson preparation are solved. The knowledge graph construction and reasoning module is used for constructing a structured knowledge graph and realizing knowledge point association mining and teaching logic reasoning; the multi-modal cognitive agent module is used for generating personalized explanation content according with a teaching target by fusing multi-modal courseware analysis, semantic understanding and lecture style dynamic adaptation functions; the lightweight digital human generation module is combined with model pruning and emotion modeling technologies to synchronously output natural voice and a high-simulation digital human image; the intelligent teaching plan auxiliary module helps the teacher to intelligently generate a teaching plan and a teaching outline according to the courseware content based on the knowledge graph.

Systems, methods, and non-transitory computer readable media including instructions for interpreting facial skin micromovements are disclosed. An example head mountable system includes a housing configured to be worn on a head of a wearer. The head mountable system also includes at least one detector integrated with the housing and configured to receive light reflections from a facial region of the head and to output associated reflection signals. The head mountable system also includes at least one microphone associated with the housing and configured to capture sounds produced by the wearer and to output associated audio signals. The head mountable system also includes and at least one processor in the housing, configured to use both the reflection signals and the audio signals to generate output that corresponds with words articulated by the wearer.

The invention relates to the technical field of computer security, and discloses a computer remote login identification device based on artificial intelligence, which comprises a user side device module used for collecting biological identification data and input behavior data of a user; the authentication and verification module is used for executing multi-mode identity authentication and dynamically adjusting an authentication strategy; the behavior analysis and risk assessment module is used for analyzing a behavior mode and a login environment of the user; the database and storage module is used for storing user identity information, behavior characteristic data and authentication logs; and the security monitoring and alarming module is used for monitoring the login activity of the user in real time and detecting potential security threats. According to the invention, the design based on multi-modal authentication greatly improves the security of remote login. By combining a plurality of authentication means such as face recognition, fingerprint recognition and voice recognition, the system can effectively prevent security vulnerabilities such as password stealing, fingerprint counterfeiting or facial image attack.

The invention provides a multi-modal sentiment analysis method and system based on attention and graph enhanced text, and belongs to the technical field of multi-modal sentiment analysis. The method comprises the steps of obtaining text features, image features and voice features in video data and performing preprocessing; an adaptive cross-modal interaction module is adopted to calculate attention weights between the text features and the image features and between the text features and the voice features, and enhanced text features are obtained; inputting the enhanced text features into a hierarchical multi-modal graph fusion network, and constructing an adjacent matrix by using a self-attention mechanism; inputting the adjacent matrix and the enhanced text features into a jump connection graph convolutional network to obtain a final feature matrix; and combining the feature matrix with the adjacent matrix, and obtaining a prediction result of sentiment analysis by using an encoder and a classifier. The problem of deficiency in context alignment among different modes is solved, and emotion polarity prediction is performed more comprehensively and accurately.

The invention relates to the technical field of AI interaction, and discloses an AI multi-modal emotion interaction memory terminal, which realizes microsecond-level synchronization of voice, facial expression and text data through a multi-thread acquisition engine, dynamically allocates each modal weight by adopting a multi-head cross attention mechanism, and adaptively adjusts modal importance based on a conversation context hidden state; when the cross-modal confidence difference exceeds a threshold value, a gating LSTM conflict resolution module is activated, and the multi-source data collaboration problem is solved; the emotional memory modeling constructs an emotional state transition topology based on a graph convolutional network, protects user privacy in combination with a differential privacy mechanism, and realizes associated event storage of millisecond backtracking of short-term memory and long-term memory. The technology integrates multi-modal dynamic perception, privacy security calculation and adaptive learning ability, significantly improves the real-time performance and personification degree of emotion interaction, and can be applied to the fields of intelligent customer service, emotion accompanying, health monitoring and the like.

The invention discloses a pig individual identification and health monitoring system based on biological characteristics, and relates to the technical field of medical treatment, and the system comprises a multi-mode biological characteristic collection module which is used for obtaining pig 3D facial characteristics, ear blood vessel distribution characteristics, gait dynamic characteristics and physiological data; the biological feature fusion recognition module is used for fusing multi-modal features based on a Transform architecture and realizing individual unique identification and feature template dynamic updating; and the digital twinborn health monitoring module is used for constructing a pig full-life-cycle digital twinborn model and realizing health state evaluation and disease early warning in combination with an LSTM neural network. According to the pig individual recognition and health monitoring system based on the biological characteristics, 1024-dimensional standardized feature vectors are constructed through cooperation of multiple devices such as an RGB-D camera, a near-infrared camera and a binocular vision sensor, modal weights are dynamically distributed in combination with a Transform network, and high-precision recognition in a complex scene is achieved.

The invention provides an examination room abnormal behavior analysis method and system for examinees in a computer examination, and the method comprises the steps: extracting the facial expression, body posture information, keyboard tapping frequency, mouse movement track and other behavior data of the examinees, and forming a comprehensive behavior feature set; a time sequence prediction algorithm based on a long and short term memory network is combined with an attention mechanism to analyze a behavior pattern in a time dimension, and abnormal behavior information is obtained; creating a target examination room environment graph through a graph neural network algorithm, analyzing abnormal behavior information, and identifying collaborative cheating behaviors; introducing a Q learning reinforcement learning framework, and dynamically adjusting a detection strategy; performing weighted fusion on the abnormal behavior information, the collaborative cheating recognition result and the detection strategy by using an extreme gradient lifting learning algorithm, calculating a comprehensive score, and generating an abnormal behavior judgment report based on the comprehensive score; according to the method, the detection capability and efficiency of the abnormal behaviors and the cheating behaviors in the online examination are improved, and the fairness and fairness of the examination can be maintained.

The embodiment of the invention provides an identity verification method and device, electronic equipment and a storage medium, and belongs to the technical field of financial science and technology. The method comprises the following steps: acquiring multi-modal biological recognition data of a user in a current environment; performing feature extraction on the face image through a convolutional neural network to obtain face features; performing feature extraction on the iris image and the fingerprint information through a support vector machine to generate iris features and fingerprint features; identifying the face posture of the user in the current environment and the environment type of the current environment; adjusting weight values corresponding to the face features, the iris features and the fingerprint features according to the face postures and the environment types; and performing weighted fusion on the face feature, the iris feature and the fingerprint feature according to the weight value, and generating an identity verification result of the corresponding user in the current environment. According to the embodiment of the invention, the security and accuracy of user identity verification can be improved, and identity verification can be intelligently and accurately carried out on users in different complex environments in a financial scene.

The invention discloses a power transmission line constructor identity verification method based on face recognition, and relates to the technical field of electric power engineering safety management, and the method comprises the steps: collecting multispectral face data, and generating a spectral stereo feature matrix; micro blood flow pulsation characteristics, skin texture characteristics and thermal imaging temperature distribution characteristics are extracted from the spectrum stereo characteristic matrix, a three-layer cascade anti-counterfeiting verification mechanism is constructed, and comprehensive living body judgment is carried out; establishing a distributed feature matching network, and matching the spectrum stereo feature matrix with a pre-stored feature library; collecting a geographic position track and an operation behavior mode of a constructor, and carrying out multi-dimensional cross validation on the geographic position track and the operation behavior mode and an identity matching result to generate a multi-level safety evaluation index; and writing the identity verification process and the multi-level security evaluation index into a distributed account book, and generating a verification voucher. According to the invention, cross analysis is carried out on the identity matching result of the constructor and the behavior characteristics, so that the problem that a traditional face recognition system is easily falsely used by the identity is effectively solved.

The invention relates to the technical field of audio and video identity recognition, in particular to an audio and video identity recognition system based on multimode clue driving. The audio and video identity recognition system comprises an audio feature extraction module, a video feature extraction module, a multimode clue fusion module, a living body detection module and an identity recognition and verification module. The audio feature extraction module is used for capturing a voice signal of a user and extracting key voiceprint features, the video feature extraction module is used for acquiring facial features or limb features of the user and extracting related features, and the multi-mode clue fusion module is used for performing intelligent weighted fusion on the features of audio and video modes. The method comprises the following steps: firstly, using a living body detection module to comprehensively analyze the dynamic characteristics of audio and video, judging whether a user is a real individual, and finally, completing the final verification of the identity of the user through an identity recognition and verification module. The problem of feature extraction and modal fusion in the prior art is effectively solved.

The invention discloses an AI auxiliary limb rehabilitation system, and belongs to the field of communication control systems, the AI auxiliary limb rehabilitation system combines a sensing module, an AI analysis module, a VR interaction module, a feedback module and the like, comprehensively collects limb movement and physiological data, introduces gamification elements, improves the patient participation degree through target setting, an award mechanism and interesting tasks, and improves the rehabilitation effect. A personalized rehabilitation plan is generated by using a deep learning algorithm, an emotion recognition unit can adjust the plan according to a psychological state, environment self-adaption and real-time adjustment of training difficulty to ensure training safety and is challenging, data cloud storage facilitates remote monitoring, third-party equipment is compatible with extended monitoring ability, and the training efficiency is improved. A brand-new, efficient and personalized solution system scheme is provided for limb rehabilitation.

The invention relates to the technical field of computer vision and artificial intelligence, and discloses a classroom teaching-oriented student dynamic expression and action recognition evaluation system and method, and the system comprises a video collection module, an image processing module, an expression and action recognition module, an evaluation analysis module, and a feedback output module. The video acquisition module is used for acquiring video data of a classroom site, the image processing module is used for preprocessing the video data, the expression and action recognition module is used for recognizing expressions and actions of students by using a deep learning algorithm, and the evaluation analysis module is used for evaluating learning states of the students. And the feedback output module outputs the evaluation result to a teacher in a chart or report form. By integrating video acquisition, image processing, deep learning recognition, data analysis and other technologies, expressions and actions of students are monitored and analyzed in real time, so that more accurate and timely learning state feedback is provided for teachers, the teachers can adjust the teaching method in time according to the results, and the classroom teaching effect can be effectively improved.

The invention provides an online car-hailing passenger position rapid positioning method based on voice interaction and visual perspective, and relates to the technical field of intelligent traffic, and the method comprises the following steps: arranging a sensor, a camera, a metal reflective curtain and a projector on a car; voice information of a driver is collected, and an intelligent voice wake-up model is designed; constructing a scenarized speech recognition model; detecting a pedestrian target for the panoramic image by using a sound-image multi-level cross-modal detection model, and obtaining appearance and posture feature detection information of the pedestrian target; a static feature matching method and a dynamic feature recognition model are designed, and passenger feature information matching is achieved; based on a matching result, determining position information of the passenger; acquiring a passenger direction taking the vehicle as a reference origin through position estimation in the panoramic image; according to the direction estimation result, a projector in the vehicle is controlled, the passenger information is projected on a curtain in the corresponding direction in the vehicle, and meanwhile voice information is used for giving a prompt.

The invention belongs to the technical field of AI interaction, and discloses an AI interaction method and system based on emotion recognition. The method comprises the following steps that in a cloud data center, an emotion relieving database is built, and an emotion AI interaction engine is built; according to the real-time multi-mode monitoring data, using an emotion AI interaction engine to generate a real-time emotion recognition result and real-time AI interaction data; according to the real-time emotion recognition result, a real-time AI response strategy is generated, and real-time emotion relieving data is extracted; generating real-time AI response data according to the real-time AI response strategy, the real-time AI interaction data and the real-time emotion relieving data; and optimizing the emotion AI interaction engine according to the real-time feedback data to obtain an optimized emotion AI interaction engine. According to the method, the problems of low emotion recognition accuracy, low AI intelligence degree and lack of an evolution function in the prior art are solved.

The invention discloses a laboratory automatic process management and multi-source data fusion system based on the Internet of Things, and belongs to the technical field of laboratory management. The system comprises a sensing layer, an analysis layer, an intelligent management layer and an application layer. The sensing layer carries out global data acquisition and real-time analysis; the analysis layer integrates data, mines an association relationship and optimizes task matching; the intelligent management layer performs full-process automatic management and control on a laboratory, and comprises an equipment cooperation module, a dynamic detection module, a dynamic configuration module, a real-time simulation module, a preset trigger module and an intelligent auditing module; and the application layer performs multi-terminal collaborative interaction and intelligent assistant assistance processing. According to the system, intelligent collaboration of laboratory equipment, automatic detection of samples, dynamic optimization of equipment parameters and automatic early warning and processing of abnormal conditions are realized through technical means such as knowledge graph construction, a digital twinborn technology and multi-mode biological recognition, and the laboratory management efficiency and the data processing accuracy are effectively improved.

It is disclosed a vehicle transportation safety monitoring system based on driver holographic management. In the system, a driver identity verification unit reads driver certificate information and / or collects driver biometric information, and compares the information with biometric information corresponding to authorized driver identity information to complete driver identity verification, a vehicle control unit determines, according to an identity verification result, whether a vehicle can be started; emotion recognition and / or behavioral intelligent analysis and / or sensor monitoring data analysis and recognition is performed, and when the result triggers a warning, an operation such as automatic speed control is performed to control the movement of the vehicle. This system can bind a driver identity with a driving permission of a designated operating vehicle, monitors a driver emotion, a driving behavior, and a fatigue status, and controls a vehicle driving status and / or sends a safety warning to a driver according to a monitoring result.

Disclosed are an affective haptic regulation system and method based on multimodal fusion, including a haptic optimal parameter adjustment module, a haptic generation module, a visual-auditory generation module, a multi-physiological signal acquisition module, a multi-sensory signal acquisition module, and a multimodal fusion emotion recognition module. The system can fuse multi-physiological signal features with audio and haptic modal features by acquiring a plurality of physiological signals of a user, accurately identify a current affective state of the user in real time through advanced data processing and analysis technology, seek for a haptic parameter with the help of an optimization theory, and achieve proactive regulation of affective state of the user; and the system can overcome the limitations of traditional subjective scale methods, effectively reduce the influence of unstable physiological signals on emotion recognition results, and significantly improve the accuracy of affective detection in the affective haptic regulation system.

The invention relates to a multi-modal fusion real-time digital human driving method based on unified behavior vector mapping, and belongs to the technical field of human-computer interaction. The method comprises the following steps of: 1) extracting multi-modal features of voice, action and vision, and generating input features by adopting language feature-emotion decoupling, staged action modeling and macroscopic-micro expression flow analysis; (2) cross-modal collaborative alignment and conflict correction are carried out, and high-precision fusion is realized through knowledge graph constraint time sequence decomposition, particle-by-particle interactive fusion and a situation-driven modal dominant strategy; and 3) constructing a three-dimensional behavior vector space, mapping the multi-modal features to a unified coordinate by utilizing honeycomb grid projection, and driving the digital human to output in combination with a coordinate-action mapping table. According to the method, the problems of difficulty in multi-modal time sequence alignment, feature isomerism and out-of-control conflict are solved, real-time interaction of naturalization, emotional and scenarized is realized, and the expressive force and adaptability of the digital human are remarkably improved.

The invention discloses an emotion detection system based on facial recognition, and relates to the technical field of computer vision and emotion calculation. A video stream time sequence analysis module is used for extracting a facial micro-expression image sequence of continuous frames, a time sequence feature vector containing a micro-expression intensity gradient, an illumination robustness coefficient and a facial action unit cooperation feature is generated, and a multi-mode dynamic sensing module is combined to carry out real-time analysis on an emotion classification probability, voice emotion parameters and physiological signals. And the fusion decision module performs dynamic weighted fusion on the multi-modal data based on the scene adaptive weight, and finally generates a comprehensive emotion score. Through multi-modal time sequence modeling and a dynamic weight optimization mechanism, the accuracy and environmental adaptability of emotion recognition are remarkably improved, and real-time perception and accurate decision making of customer emotion are realized in a target scene.

The embodiment of the invention provides an emotion recognition method and device based on artificial intelligence, and identity verification is realized through voiceprint features by creatively integrating multi-modal data of facial images, voices and body actions. And a multi-level attention fusion network is designed, and intelligent fusion of modal interior and cross-modal features is realized by using a feature attention layer and a modal attention layer. An emotion change time sequence model is constructed in combination with medical record data, the physiological indexes are fused for evaluation and correction, and dynamic evaluation and accurate monitoring of the emotional state are achieved. According to the method, the defects of the traditional technology in the aspects of multi-modal fusion, time sequence modeling, medical monitoring and the like are effectively overcome, and the accuracy and the practical value of emotion recognition in the medical scene are remarkably improved.

The invention provides a driver fatigue monitoring technology using eye movement tracking, and relates to the field of auxiliary driving, and the technology comprises the steps: collecting a face image frame through a front-end camera, carrying out Medipe (Media Pipeline) detection, recording a head posture, and recognizing an iris center region; extracting continuous eye closing time and mouth state information of the driver through the face information; a PNP algorithm is used; a world coordinate system and a camera coordinate system are established, and the sight line vector and the external environment are located in the same space; modeling is carried out on the human eyes and the fixation area; coordinates of a fixation point in a three-dimensional space are obtained through calculation of the geometric model; real-time attention analysis is carried out through the fixation point, and an analysis result is determined; the attention change condition and the fatigue state of the driver are effectively recognized through a BP neural network in combination with a fixation point analysis result. And early warning measures are taken in time to remind a driver. The system is crucial for improving the driving safety, and necessary warning and intervention can be provided when the driver is distracted or fatigued.

The invention belongs to the technical field of intelligent education, and particularly relates to a classroom attention detection method and system based on multi-modal data fusion. Aiming at the problems of high equipment cost, low multi-source data fusion efficiency, insufficient privacy protection and the like in the prior art, the invention provides the following solutions: collecting face, eye movement, posture, voice signals and heart rate variability data of a student through a sensor; multi-modal data synchronization is realized by adopting a time sequence alignment algorithm; respectively extracting a visual attention feature, a voiceprint matching feature and a physiological wake-up feature by using a lightweight deep learning model; constructing a multi-modal data fusion network, and dynamically adjusting a feature weight in combination with a classroom scene; attention anomaly detection is realized by adopting a hybrid model, and real-time early warning is output through edge computing equipment. The method has the beneficial effects that the hardware cost is greatly reduced while the detection precision is ensured, and the privacy of students is effectively protected; a dynamic weight distribution mechanism improves the adaptability of different teaching scenes.

The invention provides a network advertisement effect evaluation method based on eye movement tracking, and aims to improve the advertisement putting accuracy and the effect evaluation accuracy through integrating eye movement data and emotion data, and the method comprises the steps of capturing an eye movement track by using an infrared camera, extracting eye movement feature points, and calculating a fixation point and time. Meanwhile, emotion data are collected through a facial expression recognition technology and a physiological sensor, a visual attention model and an emotion analysis model are constructed through a deep neural network, the reverse emotion and cognitive avoidance state of the user is analyzed, and advertisement content display is dynamically adjusted; according to the method, the user position and time data are combined, personalized advertisement putting is achieved, the advertisement effect is optimized through multi-dimensional data analysis, the user experience is improved, and a scientific basis is provided for advertisement decision making.

The invention discloses a palm vein and palm print feature fusion intelligent identity verification method and system, and relates to the technical field of biological recognition, and the method comprises the steps: collecting a palm image and identity data of a to-be-authenticated user, and carrying out the identity coding of the palm image based on the identity data, and obtaining a palm identity coding image set; then, through region marking and feature extraction, multi-dimensional features of palm veins and palm prints are obtained; then, performing spatial alignment fusion on the features of the two to generate a palm code fusion feature data set; and then, through encryption processing and network training, a self-adaptive palm identity recognition network is obtained. And finally, acquiring a palm image of a target user, and performing identity verification by using the network. The technical problems of insufficient safety, low accuracy and poor anti-interference capability caused by dependence on a single biological characteristic in an existing identity authentication method are solved, and the technical effect of improving the safety, the accuracy and the anti-interference capability of identity authentication is achieved.

The invention discloses an emotion pressure recognition method and system based on an action form fusion graph neural network, and belongs to the field of image processing and machine learning. A double-graph data structure is constructed, wherein graph data with human body key points as nodes and graph data with image frames as nodes are processed through T-GCN and PCSN respectively. The T-GCN model fuses a GCN layer and a Mama encoder, the cohesion and coupling ability of emotion category characterization is enhanced through a graph partitioning strategy, and the spatial-temporal characteristic relation of human body actions is effectively captured. The PCSN adopts a three-branch parallel structure including one-dimensional global average pooling, 3 * 3 convolution and 5 * 5 convolution, efficient extraction of non-homogeneous features of the human body, the face and the hand is achieved, and the long and short distance dependence and local cross-channel interaction relation of the global features is established. According to the system, through combination of transfer learning and small sample learning, a public data set is utilized to initialize a network, and secondary training is performed on a small sample data set of campus emotion pressure, so that the recognition precision is remarkably improved.

The invention relates to the technical field of digital interaction, in particular to a digital interaction enhancement system based on multi-modal voice, which comprises a multi-modal sensing module comprising a voice sensing unit, a visual sensing unit and an environment sensing unit and used for capturing multi-modal data input by a user; the multi-modal fusion module comprises a data synchronization unit, a feature extraction unit and a multi-modal fusion unit and is used for performing time synchronization, feature extraction and fusion processing on the multi-modal data; and the interactive understanding module comprises a natural language processing unit, a context understanding unit and an emotion analysis unit and is used for understanding the intention and the emotion state of the user. The deep learning technology is adopted to fuse voice, vision and environment data, and efficient and natural user interaction experience is achieved through the steps of feature extraction, feature fusion, intention recognition and the like. The system has excellent performance and a wide application prospect in a complex environment.