583results about "Multiple biometrics use" patented technology

The invention discloses an intelligent glasses image adjusting system based on eye movement tracking and gesture fusion, and relates to the technical field of intelligent equipment. A multi-modal sensing module is arranged to construct a multi-modal sensing layer to capture eyeball movement tracks and gesture actions; a fixation point prediction module is set to process a dynamic scene through a space-time attention mechanism to obtain a fixation point prediction area, a gesture semantic understanding module is set to process gesture actions based on a Transform architecture, and the gesture actions of a user are converted into image adjustment instructions. An image enhancement strategy setting module designs a multi-stage image enhancement strategy according to the fixation point prediction area and the image adjustment instruction, and sets a dynamic adjustment intensity control module to perform adaptive adjustment to obtain a dynamic adjustment intensity control result; an eye movement-gesture cooperative control module is arranged to provide an eye movement-gesture cooperative control mechanism to realize image area selection and parameter adjustment, and accurate image area selection and parameter adjustment are realized.

The invention belongs to the technical field of artificial intelligence and health monitoring, and particularly relates to an emotion recognition and intervention system based on facial micro-expression and physiological signal fusion. The emotion recognition precision is improved through space-time alignment analysis of facial micro-expressions and physiological signals, adaptive feedback is achieved in combination with cognitive load correlation modeling and a wearable multi-channel regulation and control terminal, cross-period emotion evolution prediction and group situation awareness are supported, a'awareness-decision-intervention 'complete closed loop is constructed, and the emotion recognition efficiency is improved. And the accuracy and initiative of emotion management in a complex environment are enhanced.

The invention relates to the field of multi-modal sentiment analysis, in particular to a multi-modal sentiment analysis method based on task association perception learning. According to the scheme, the method comprises the steps that multi-modal data including text, audio and visual modal data are obtained, feature extraction is conducted on the multi-modal data, a double-branch comparison module is constructed for each kind of modal data, each double-branch comparison module comprises a fusion branch and a comparison branch, and the fusion branch is connected with the comparison branch; input of a fusion branch and a comparison branch of each double-branch comparison module is an extracted text modal feature, an extracted audio modal feature and an extracted visual modal feature; and finally, feature fusion and prediction output are carried out, so that the accuracy and robustness of multi-modal sentiment analysis are improved. The method is suitable for multi-modal sentiment analysis.

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

The invention belongs to the field of man-machine interaction, and particularly discloses a man-machine interaction method for electronic screen control, which comprises the following steps: acquiring touch track data of a user according to a touch sensor, and performing feature extraction on the touch track data to obtain a touch feature vector; the method comprises the following steps: acquiring gesture action data of a user according to image acquisition equipment, and performing key point detection on the gesture action data to obtain a gesture feature vector; the method comprises the following steps: acquiring voice instruction data of a user according to an audio acquisition device, and performing acoustic feature extraction on the voice instruction data to obtain a voice feature vector; acquiring current environment parameters in real time through an environment detection module, wherein the environment parameters comprise illumination intensity, environment noise level and distance between a user and a screen; the objective of the invention is to solve the problem of insufficient reliability of a man-machine interaction mode in a complex environment in the prior art.

The invention relates to the technical field of vehicle control, in particular to a vehicle control method, a vehicle and electronic equipment. The control method comprises the following steps: acquiring a voice instruction of a person in a vehicle, converting the voice instruction into a text instruction, analyzing a user intention in the text instruction, and extracting a function object, the function intention and an associated main body in the user intention; determining a personnel node corresponding to the associated subject in the cabin personnel relation graph, when the personnel node corresponding to the associated subject is not unique, determining the personnel node referred by the associated subject based on a preset multi-priority decision model, and extracting position features corresponding to the personnel node in the cabin personnel relation graph, and generating a control instruction based on the position feature, the function object and the function intention. According to the invention, specific passenger identities involved in complex semantics can be accurately understood, and directional control is executed according to the specific passenger identities, so that the intelligent degree of voice interaction and the user experience are greatly improved.

The invention belongs to the field of biomedical engineering, particularly relates to a multi-modal emotion recognition method based on electroencephalogram signal and facial expression fusion, and aims to improve the accuracy of emotion recognition. The method comprises the steps that electroencephalogram signals and a face video of a subject are collected, frequency band extraction, time window division and standardization processing and key frame extraction and face embedding coding are conducted respectively, time synchronization is achieved through data alignment, electroencephalogram time sequence fragments are input into a preset coding network to extract electroencephalogram time sequence features, and the electroencephalogram time sequence features are obtained. Inputting the facial spatial feature matrix into a preset convolution and time sequence fusion network to extract facial spatial and temporal features, then realizing dynamic interaction between modals through a cross attention mechanism to obtain preliminary fusion features, obtaining modal confidence degrees corresponding to the modals through a preset regression network, weighting the electroencephalogram feature matrix and the facial expression feature matrix, and obtaining an electroencephalogram feature matrix and a facial expression feature matrix; and outputting a final fusion feature matrix. And inputting the final fusion feature matrix into a preset classifier for emotion category prediction, and outputting an emotion recognition result.

The embodiment of the invention provides an identity verification method and device, equipment, a medium and a product, and relates to the field of biological recognition. The method comprises the following steps: acquiring multi-modal biological characteristic data of a user, and analyzing the multi-modal biological characteristic data through a living body detection technology; wherein the multi-modal biological characteristic data comprises a combination of at least two of the following biological characteristics: fingerprints, irises, voiceprints and facial images; according to the type of a service to be executed by the user, dynamically adjusting a priority strategy of biological feature verification; and generating an identity verification result based on the living body detection result and the priority strategy. According to the scheme of the invention, through combination of the multi-modal biological characteristic data and integration of a living body detection technology, diversity and anti-cheating capability of identity verification are enhanced, meanwhile, the dynamic priority strategy can be adaptively adjusted based on a service type, a verification process is optimized on the basis of improving verification safety, and thus reliability of identity verification is improved.

A method for generating reference biometric data based on a bending of a flexible device is described. In one aspect, a method includes forming training data includes bending estimates of a flexible device worn by a first user, training a model based on the training data, and generating reference biometric data for the first user based on the model.

The invention relates to the technical field of education and teaching. The invention provides a classroom teaching dynamic feedback and evaluation system integrated with multi-modal emotion calculation. The system comprises a multi-modal data acquisition module used for acquiring multi-source data in a classroom teaching environment and sending the multi-source data to an emotion-cognitive calculation center; the emotion-cognition calculation center is used for carrying out fusion analysis on the multi-source data, identifying the emotion state and the cognition state of the student, carrying out attribution analysis through an emotion-cognition coupling model, generating a coupling state label for describing the specific reason of the learning dilemma, and sending the coupling state label to the dynamic intervention engine; the dynamic intervention engine is used for matching and generating individual-level, group-level and system-level teaching intervention instructions from the teaching strategy knowledge base according to the received coupling state labels; and the visual feedback and evaluation module is connected with the emotion-cognition calculation center and the dynamic intervention engine, and is used for providing real-time classroom state visual display for the teacher and pushing personalized learning suggestions to the student terminal.

The invention discloses a robot emotion understanding and responding method, which comprises the following steps of: according to obtained multi-source data, setting a fusion initial weight through data integrity, and adjusting the fusion initial weight according to data confidence and environment quality to obtain a fusion weight so as to perform cross-modal feature fusion; according to the fusion feature, mapping to a two-dimensional emotion space, selecting a point with the maximum activation degree, and generating an emotion vector; based on the emotional state sequence, a response strategy vector is generated through a Linear Attention time sequence strategy network; based on a unified condition generation network, fusing the style vector and the response strategy vector to obtain an executable multi-mode instruction; calling a hierarchical memory network to obtain a short-term memory slot and a long-term sequence modeling unit which are used for modulating an executable multi-modal instruction; wherein the hierarchical memory network is trained and optimized through a loss function, and the loss function at least comprises style consistency loss, content consistency loss and memory retention loss.

The invention relates to the technical field of identity recognition, in particular to an identity recognition method and system based on multi-modal data. The invention provides an identity recognition method based on multi-modal data, which adopts a user use dimension, a user interaction dimension and a user voiceprint dimension, thoroughly avoids legal compliance risks caused by a face recognition technology, constructs a non-biological recognition system based on sensor motion trail capture, touch screen interaction behavior modeling and voiceprint feature extraction, and improves the recognition efficiency. Therefore, all the feature data cannot restore the original biological attributes, and the hidden danger of privacy litigation is eliminated from the source. Meanwhile, depending on a three-dimensional dynamic fusion mechanism of the sensor, the touch screen and the voiceprint, full-scene precision jump, compensation of the dark light defect by the motion inertia of the sensor capture equipment, noise interference resistance by a touch screen track modeling fine operation habit and physiological uniqueness abstract representation of the voiceprint spectrum are realized, and the three forms a complementary enhancement effect through dynamic weights.

The invention relates to an entry and exit certificate verification method based on dynamic weight and multi-modal fusion, and belongs to the technical field of entry and exit certificate information processing and verification. The method comprises the following steps: collecting multi-source heterogeneous data of a certificate in three modes of visual reading, machine reading and chip reading; constructing a verification data graph taking the unique identification of the certificate as a core, and carrying out data association and alignment; based on a dynamic weight distribution model, performing weighted voting fusion on the homologous key fields, and performing semantic fusion on the heterologous complementary fields; and constructing a traceable verification decision factor matrix, judging a verification conclusion according to the content of the matrix, positioning a source when the verification conclusion is abnormal, and generating a structured verification record containing a complete decision path and a disposal scheme. Through the dynamic weight fusion and the decision factor matrix, the problem of splitting of the existing verification mode is effectively solved, and the accuracy and reliability of the verification result and the process traceability are remarkably improved.

The invention discloses an emotion analysis method based on a multi-modal large model. The method comprises the following steps: S1, extracting multi-modal emotion features; respectively designing special emotional feature extractors for three modes of facial expression, voice and text; s2, carrying out cross-modal emotion alignment and fusion; mapping the emotion features of different modes to a unified emotion semantic space; s3, an emotion inconsistency detection mechanism; the method is specially used for detecting the emotion inconsistency phenomenon between different modes. S4, fine-grained sentiment classification is carried out; the sentiment classifier comprises three sub-tasks of basic sentiment classification, complex sentiment recognition and sentiment intensity regression; s5, a model fine tuning training strategy; and optimizing the performance of the model by adopting a multi-stage fine-tuning training strategy. According to the method, deep fusion and accurate analysis of facial expressions, voice acoustic features and text semantic information are realized, so that a complex emotional state and an emotional inconsistency phenomenon are effectively recognized.

The invention discloses a multi-modal sentiment classification method based on multi-view interaction representation, and relates to the technical field of multi-modal information processing, and the method comprises the steps: collecting and preprocessing the voice, facial expression and text data of a user, and generating a multi-modal data packet; based on the multi-modal data packet, performing feature extraction by adopting a hierarchical attention mechanism to obtain an aligned voice expression text feature sequence, and generating a multi-modal feature flow; performing sentiment classification on the sentiment benchmark result by adopting a time sequence gating network to obtain a service state vector and a conflict view vector; according to the multi-view fusion emotion feature sequence, a time sequence gating network and a multi-mode recognition model are adopted for recognition, and a matched emotion classification result and a natural language reply are generated in combination with the service state. According to the method, sentiment classification is carried out on the sentiment benchmark result by adopting the time sequence gating network, the service state vector and the conflict view vector are obtained, and synchronous description of the sentiment state and the context is realized.

The present application belongs to the field of artificial intelligence and health monitoring technology, and specifically relates to an emotion recognition and intervention system based on facial micro-expression and physiological signal fusion. The disclosed system integrates multi-modal synchronous acquisition, dynamic feature fusion decoding and individualized closed-loop intervention functions. Through the spatio-temporal alignment analysis of facial micro-expression and physiological signals, the emotion recognition accuracy is improved. Combined with cognitive load correlation modeling and wearable multi-channel regulation terminals, adaptive feedback is realized, cross-period emotion evolution prediction and group situation awareness are supported, a complete closed loop of "perception-decision-intervention" is constructed, and the accuracy and initiative of emotion management in complex environments are enhanced.

The invention provides a multi-modal behavior recognition method based on progressive guide fusion, and belongs to the technical field of computer vision and artificial intelligence. The method comprises the following steps: performing skeleton-guided dynamic cutting preprocessing on input RGB data; extracting RGB and skeleton features through a WTSP enhancement network and an MSWTNet respectively; the extracted features are transmitted to a progressive guidance fusion network, skeleton features are strengthened through skeleton self-guidance, and then bimodal features are integrated through skeleton guidance RGB frequency domain fusion; and finally, behavior recognition is completed by using a space-time classification module. According to the method, the complementarity of RGB and skeleton data is fully mined, and the accuracy and robustness of multi-modal behavior recognition are effectively improved. According to the method, the accuracy rate of the NTU-60C-sub protocol reaches 95.8%, the accuracy rate of the C-view protocol reaches 98.6%, the calculated amount is reduced by 20% or above, and the method is suitable for intelligent monitoring, medical rehabilitation and other scenes.

The invention discloses an anti-fatigue driving signal lamp and atmosphere lamp control method and a vehicle, and relates to the technical field of vehicle intelligent auxiliary driving and active safety, and the method comprises the steps: collecting a cab video stream, and positioning face key points after image preprocessing; calculating an eye PERCLOS index and a mouth yawning frequency, and dynamically adjusting a weight based on a face angle so as to fuse and calculate a comprehensive fatigue index; carrying out grading judgment on the index by utilizing a hysteresis interval containing a starting threshold value and a reset threshold value, and reducing a judgment threshold value in real time by utilizing a linear model according to the vehicle speed; and driving an indoor atmosphere lamp to execute a breathing or stroboscopic mode according to the judged sober, mild or severe fatigue grade, controlling an outdoor signal lamp and a road surface projection assembly to execute corresponding warning actions, and performing reverse pre-distortion processing on a projected image. According to the invention, through multi-source feature fusion, hysteresis threshold determination and vehicle speed correction mechanisms, the stability of fatigue monitoring and the accuracy of warning are improved.

The invention discloses an access control method based on artificial intelligence, and relates to the technical field of access control. When the personnel in the monitoring range stay for a preset time length or press a physical key, triggering an access control request, collecting a video frame containing a human face and extracting a human face image; ambient brightness is measured through a photosensitive sensor, illumination compensation is performed before graying is performed when the ambient brightness is lower than a threshold value, and otherwise, graying is performed directly to obtain a grayed face image; identifying the grayed face image by using a local shielding face identification method, if the identification succeeds, acquiring a personnel ID, and if the identification fails, entering a server side for secondary identification; sending the grayed face image to a server, performing secondary recognition through a deep metric learning algorithm, and if the recognition succeeds, obtaining a personnel ID; and fingerprint composite verification: when any end is successfully identified, acquiring a personnel fingerprint image and acquiring a real-time fingerprint, and opening an access control after successful matching. According to the invention, efficient and safe access control is realized through a dual-biological characteristic cooperation mechanism combining face preliminary verification with fingerprint accurate confirmation.

A system for quantum-enhanced behavioral analysis and secure database operations includes an ACAARY Database configured to store and process quantum-verified data under multi-tiered access control; an AR Quantum Cyber-Micro Expressions System configured to analyze involuntary behavioral patterns in digital communications via computational analysis of user interaction data; AR Quantum Sequence Triplications with Quantum Operations comprising three-qubit verification circuits implemented on superconducting quantum processors that perform parallel artificial-qubit processing to achieve unbiased quantum supremacy; an AR Quantum Parallel Data Algorithm Structure and Method Specialized Encrypted Algorithmic Quantum Procedures configured to execute simultaneous perpendicular encryption processing with real-time transaction verification across multiple channels; an AR Helmsmen Quantum Transportation Management System with integrated digital licensing and frequency-based quantum routing protocols; and an AR Thermodynamics Devices System configured to perform thermal bonding and division operations while maintaining different temperatures within defined containment areas for material modification.

The invention relates to the technical field of intelligent breeding, bioinformatics and artificial intelligence crossing, provides a chicken coccidiosis early warning method, device and equipment, a storage medium and a program product, and has relatively high anti-interference capability during chicken coccidiosis early warning. The voiceprint feature vector and the posture feature vector of the chicken are obtained according to the sound wave data and the video data in the current time window, so that the disease posterior probability and the group infection index are obtained; inputting the group infection index into a preset chicken coccidiosis SIR-Coccidia propagation dynamical model to carry out epidemic situation evolution simulation of a future time step; and when the epidemic situation evolution simulation result exceeds a preset safety threshold, generating a preliminary prevention and control strategy based on a multi-objective optimization algorithm, performing compliance matching processing on the preliminary prevention and control strategy and a preset veterinary prevention and control knowledge base, obtaining a corrected prevention and control strategy, performing risk prediction, and generating a target early warning instruction set.

The invention discloses an agricultural machinery driving fatigue grade division method based on multi-source physiological information, solves the problems of insufficient robustness and detection precision of the agricultural machinery driving fatigue grade division method, and belongs to the technical field of human-computer interaction, traffic safety and artificial intelligence cross application. The method comprises the steps that electrocardiosignals and face image data of a driver are collected, feature extraction is carried out, and a heart rate feature vector and a face feature vector of a current sliding window are obtained; fusing the standardized heart rate features and facial features to obtain fused feature representation; taking the fusion feature representation as an observation vector, inputting the observation vector into a time sequence model, and estimating potential fatigue strength; according to the standardized heart rate features and facial features, obtaining respective corresponding edge fatigue probabilities, and fusing the two edge fatigue probabilities into a joint fatigue confidence coefficient; and calibrating the potential fatigue strength by using the joint fatigue confidence, and carrying out ordered grading by using the calibrated fatigue strength to obtain a predicted fatigue grade.

The invention provides an information processing system. The information processing system comprises an image acquisition device used for acquiring front image data; the analysis server device is used for analyzing the collected image data; the voice output device is used for carrying out voice prompt on visually impaired people according to the analyzed image data; and the display device is used for performing augmented reality display according to the analyzed image data.

Provided herein are systems and methods for authenticating users during payment transactions and other types of interaction events using biometric information. The disclosed systems involve a biometric orchestration platform that receives biometric data of a user from a biometric module or device, intelligently routes this data to a biometric verification service, and retrieves a positive identification if a match is found. This identification is then used in various ways, e.g., to retrieve a payment card token linked to the user's authorized payment method.

A multimodality face liveness detection method to prevent biometric attacks on electronic identification authentication systems comprises 5 steps. Step 1: Training the backbone model for feature extraction, Step 2: Semi-automatic data preprocessing, Step 3: Data normalization and augmentation, Step 4: Building a deep learning model (BiMoTranS) for multimodal face liveness detection based on Transformer architecture with pre-training using the self-knowledge-distillation method, Step 5: Training the multimodal model using multi-modal data fusion techniques.

The described voting system comprises two main components: an online verification device and an offline voting device. The verification device utilises a data interface to communicate with central databases for real-time voter verification, employing biometric sensors to ensure accurate voter identification and prevent duplicate voting. This system enhances security by generating a unique verification code for each verified voter. The offline voting device, designed to be immune to network-based threats, uses this code along with biometric verification to authenticate voters. It features a user interface that employs eye gesture technology, allowing voters to select candidates privately and securely through eye movements, without any visible indication of their choices to onlookers. This setup not only protects the integrity of the vote from cyber threats but also safeguards voter privacy and reduces the potential for voter intimidation, for a free, fair, and secure voting process.

The invention provides an emotional feature recognition system and method for classroom teaching. The method comprises the following steps: acquiring facial image data and behavior time sequence data of a student in a classroom teaching process through a camera after the student clearly knows and agrees to authorize; determining dynamic expression features of the student through a preset facial analysis model according to the facial image data; on the basis of a head posture change sequence and an eye fixation focus track extracted from behavior time sequence data, constructing concentration indexes and confusion indexes of students in different teaching links through multi-feature fusion; and when it is detected that a preset condition is met, generating an emotional feature tag of the student in classroom teaching based on the dynamic expression feature, the concentration index and the confusion index, and pushing the emotional feature tag to a teacher terminal for visual display. By adopting the scheme of the invention, multi-modal feature fusion analysis of the classroom state of the student can be realized, so that the accuracy of classroom teaching regulation and control is improved.

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 discloses a target state parameter determination method and device, a storage medium and an electronic device, and relates to the technical field of smart home, and the target state parameter determination method comprises the steps: obtaining a target face video stream of a target object outputted by an image collection module, obtaining a light supplementing state signal output after the light supplementing module supplements light to the space where the target object is located; determining a light supplement condition corresponding to each frame of image in the target face video stream according to the light supplement state signal, and determining a reference light intensity offset of a forehead area and a nose root area in each frame of image according to the light supplement condition corresponding to each frame of image; and according to the reference light intensity offset of the forehead region and the nose root region in each frame of image, pixel-by-pixel correction is performed on the pixel brightness value of each frame of image to obtain a corrected video stream, and a target state parameter of the target object is determined according to the corrected video. By adopting the technical scheme, the problem that the state parameters of the target object cannot be accurately determined is solved.

User enrollment to a biometric identification system begins with a pre-enrollment process on selected general input devices (GID) such as smartphones. The user may enter identification data such as their name and use a camera of the GID to acquire first image data, such as of their hand. The first image data is processed to determine a first representation. Upon presentation of a hand at a biometric input device, second image data is acquired. The second image data is processed to determine a second representation. If the second representation is deemed to be associated with the first representation, the enrollment process may be completed by storing the second representation for subsequent use.