832 results about "Facial characteristic" patented technology

The invention relates to the field of road traffic, in particular to a multi-feature fusion fatigue driving detection method and a fatigue driving detection system. The method comprises the following steps: extracting facial features from a face image of a driver; extracting vehicle features from the vehicle driving parameters of the vehicle driven by the driver; and fusing the facial features and the vehicle features to judge whether the driver is in fatigue driving. Extracting facial features by designing a CNN model; extracting basic convolution features; extracting local convolution features; extracting global convolution features; performing pooling operation; aggregating global features; and carrying out dimensionality reduction mapping. A self-encoder is designed to extract vehicle characteristics; a symmetric deep neural network structure is adopted, and high-dimensional time sequence data is compressed to a low-dimensional potential space through nonlinear mapping; through combination and matching of the CNN model and the auto-encoder, the technical defects of feature redundancy, noise interference, information loss and suboptimal decision existing in an existing multi-feature fusion fatigue driving detection system are thoroughly solved.

The invention discloses a campus face recognition abnormal behavior monitoring method and system based on artificial intelligence, and the method comprises the steps: collecting the video stream data of a person in a campus in real time, and recognizing the behavior characteristics of the person in a video frame in real time through employing a lightweight target detection algorithm; extracting facial features of the personnel by using a pre-trained face recognition model, and recognizing identity information of the personnel; generating a personnel behavior semantic description vector with a spatio-temporal context based on the behavior characteristics and the identity information; inputting the personnel behavior semantic description vector into a dynamic early warning threshold engine, and outputting an abnormal behavior probability and risk level evaluation result; and triggering an early warning information pushing mechanism according to the abnormal behavior probability and the risk level evaluation result, and sending early warning information to a corresponding responsible person terminal. By using the embodiment of the invention, the accurate and automatic association of the abnormal behavior and the personnel identity can be realized, and the early warning accuracy, the handling response speed and the system adaptive ability of campus safety monitoring are improved.

The invention provides an interactive classroom student dynamic portrait generation method, a medium and a system, and belongs to the technical field of student dynamic portrays.The method comprises the steps that a student facial image sequence is collected, facial feature points are extracted to construct an expression feature matrix, and sight line focus data are obtained by combining an eye movement tracking technology to calculate an eye light concentration degree index; and establishing an individual association model, identifying specific expressions and analyzing attention fluctuation characteristics. And fusing multi-dimensional features such as an expression similarity vector and an eye light concentration index into a dynamic attention state score. Learning state evaluation is realized based on a multilayer bidirectional converter network, wherein multi-head attention mechanism parameters are determined by expression change frequency, an eye light concentration threshold and an attention fluctuation rate. And finally, generating a student dynamic portrait feature map, and forming a classroom interaction effect evaluation index through clustering analysis, thereby solving the technical problem that the dynamic change of the student classroom attention state is difficult to accurately evaluate based on a single feature.

The invention discloses a multi-modal emotion recognition method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring text, video and audio data of a user and respectively performing feature extraction to obtain text features, audio features and facial features; the three features are input into a pre-trained multi-modal emotion recognition model, the multi-modal emotion recognition model comprises a first fusion module, a second fusion module, a third fusion module, a fourth fusion module and a classification module, the audio features and the text features are fused through the first fusion module, and audio text features are obtained; fusing the facial features and the text features by using a second fusion module to obtain facial text features; performing feature enhancement on the text features by using a third fusion module to obtain enhanced text features; fusing the audio text features, the face text features and the enhanced text features by using a fourth fusion module to obtain multi-modal features; and classifying the multi-modal features by using a classification module to obtain a sentiment classification result of the user.

The invention relates to the technical field of identity authentication, and discloses an identity authentication method and system based on a large-model multi-mode venue, and the method comprises the steps: obtaining a fingerprint image, a face image and behavior interaction data of a user; performing fingerprint feature extraction according to the fingerprint image to obtain a fingerprint feature vector; performing facial feature extraction according to the facial image to obtain a facial feature vector; performing feature fusion on the fingerprint feature vector and the face feature vector, and generating a fusion feature vector after data dimension reduction; performing behavior feature extraction according to the behavior interaction data to obtain a behavior feature vector; and after cross-modal mapping is carried out according to the fusion feature vector and the behavior feature vector, matching degree analysis is carried out in a preset unified semantic space, and an identity authentication result is obtained. The method has the following effect that the identification security of identity authentication can be improved.

The invention discloses an angry emotion recognition method based on a video stream, and belongs to the technical field of emotion recognition. The method comprises the following steps: preprocessing a video stream of a visitor to obtain a time-space aligned standardized facial image sequence, a 3D skeleton sequence and a voice segment; performing feature extraction on the standardized facial image sequence by using a 3D-ResNet34 network and a micro-expression optical flow enhancement algorithm to obtain optical flow facial features; performing feature extraction on the 3D skeleton sequence by using a space-time diagram convolutional network to obtain limb action features; performing feature extraction on the voice segments to obtain voice features; and performing fusion processing on the facial features, the body movement features and the voice features by using a multi-head space-time cross attention mechanism and an expansion time sequence convolution algorithm, and outputting an angry emotion recognition result through dynamic weight distribution classification. According to the method, the angry emotion recognition accuracy of the system is greatly improved on the whole.

The invention discloses a facial emotion analysis method based on spatial-temporal feature network construction. The method comprises the following steps: S1, video frame segmentation and feature extraction; s2, dividing the video into a plurality of time periods according to the set time window length and time step length; and for each time period, constructing a facial feature network representing association between facial features based on the feature sequences of all frames in the time period. The networks obtained through calculation in all the time periods are synthesized, and a dynamic network sequence is obtained; s3, performing dynamic network feature extraction and dynamic key point analysis based on the facial feature dynamic network; and S4, performing emotion calculation or psychological state evaluation based on the dynamic network features or the dynamic key points. The method solves the problems that an existing method is difficult to comprehensively capture complex dynamic characteristics of facial expressions and lacks dynamic correlation modeling and dynamic key point recognition capabilities.

The invention relates to the technical field of intelligent detection, in particular to an edge fatigue detection method and device based on adaptive threshold adjustment and a medium. The method comprises the following steps: acquiring facial image data and facial infrared data of a driver in a real-time state; preprocessing the face image data and the face infrared data; performing facial mark point positioning on the preprocessed facial image data and facial infrared data through a facial feature detection model to obtain an enhanced 3D mark point image; comparing the enhanced 3D mark point image with a physiological feature database to obtain physiological data; and calculating the physiological data through a dynamic threshold decision model and a multi-index fusion engine to obtain a fatigue decision. According to the method, the environmental adaptability can be improved, individualized precise adaptation is realized, precise adaptation is realized for individual differences, and the multi-working-condition robustness is enhanced.

The invention relates to the technical field of bionic humanoid robots, in particular to a multi-expression driving bionic intelligent robot head which comprises an eyebrow movable layer, a cheekbone movable layer, an eye movable layer, an upper lip movable layer and a chin movable layer located below the upper lip movable layer, and the eyebrow movable layer, the cheekbone movable layer, the eye movable layer and the upper lip movable layer are spliced and assembled by hexagonal threaded copper columns through reserved hole positions. The robot head is composed of a shell, an aluminum alloy machining plate frame, a CNC metal part, a steering engine, a rail and a pull wire, and a camera module and a voice control module are arranged in the robot head. The shell can be customized according to different figure facial features, the shell is of a high-precision, compact and adjustable structure, 26 steering engines are integrated in a limited space to control opening and closing of the face including left and right eyeballs, upper and lower eyelids, canthus, eyebrows, cheeks, mouth corners and mouth and actions of lips, and the layered design and control mode of the head of the robot enable the robot to be more intelligent. And the mounting, the dismounting and the maintenance are more convenient.

The invention discloses a face image reconstruction method based on semantic identity feature decoupling and consistency reservation of a diffusion model, and the method comprises the steps: 1, obtaining and preprocessing a face image set of identity labeling, and generating a face feature point distribution diagram, a semantic mask diagram and a description text; 2, multi-modal features are extracted and fused through a semantic identity extraction network; 3, carrying out noise adding and de-noising processing by utilizing a diffusion model, and combining a reconstructed network and semantic identity loss optimization; and 4, face image reconstruction is completed. According to the method, in the face image reconstruction process, the driving requirements of semantic information such as texts for image editing can be accurately captured, fine-grained semantic features and identity features are decoupled, the core identity features of the face can be effectively reserved, and loss of identity consistency caused by semantic editing is avoided; therefore, technical support is provided for application scenes with high requirements on face identity accuracy in the field of computer vision, and the reliability and practicability of face image reconstruction are improved.

The invention discloses a diagnostic wax pattern digital manufacturing method and system, a product and a medium, and relates to the field of image processing.The method comprises the steps that oral cavity data of a target patient are obtained according to a three-dimensional oral cavity scanner, facial feature data of the patient are synchronously collected through a facial scanner, and the oral cavity data are converted into a first digital model; converting the facial feature data into a second digital model, extracting key parameters of teeth of the target patient based on the first digital model, marking a to-be-modified area on the first digital model based on the personalized demand information, extracting a key control point in the to-be-modified area, extracting a repair parameter standard value from a clinical case database, and repairing the target patient according to the repair parameter standard value. Constructing an initial digital veneering model based on the parameter standard values; and converting the personalized demand information into a target adjustment amount of the key control point, and modifying the initial digital veneering model according to the target adjustment amount to obtain a digital diagnosis wax pattern. By implementing the method, the manufacturing accuracy of the oral cavity repairing and diagnosing wax pattern can be improved.

The invention discloses a student state real-time analysis method and device based on deep learning, and the method comprises the steps: obtaining and preprocessing image data of students in a classroom, and carrying out the multi-scale face detection and facial feature extraction of the preprocessed image data; expression features are extracted based on standardized facial feature data, fusion is carried out in combination with attention indexes and attitude features, a time sequence feature sequence is constructed, a heavy-tailed recurrent neural network is applied to carry out time sequence modeling, an evaluation standard is established, evaluation parameters are adjusted through a self-adaptive threshold value, and finally a student state evaluation result is obtained. Through the heavy-tailed recurrent neural network and a slow transition mechanism to low-dimensional chaos, subtle changes and long-term trends of student states can be accurately captured, and the technical problems that a traditional student state monitoring method is poor in real-time performance, limited in coverage and insufficient in individuation are solved.

The invention discloses a quick start-up control method of a face recognition intelligent lock system, which comprises the following steps: enabling the face recognition intelligent lock system to be in an ultra-low power consumption sleep mode in which only a trigger sensing unit is kept for power supply in a normal state, and when the trigger sensing unit detects that a person approaches, executing hardware-level quick wake-up processing of skipping a redundancy self-checking process on the system, entering a system wakeup initialization completion state to execute core module preloading and algorithm component ready processing of preloading a human face recognition core algorithm component on the human face recognition intelligent lock system, and entering a human face recognition module ready state to enable the core module to execute facial feature collection and compare with a local feature library; if the comparison is passed, a power supply self-holding circuit is triggered, and power supply self-holding and unlocking driving linkage processing of maintaining power supply of an unlocking driving unit to complete unlocking and cutting off a power supply of a face recognition unnecessary module is executed on the face recognition intelligent lock system, so that the face recognition intelligent lock system enters a dormant state of unlocking completion and system low consumption.

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.

In an embodiment, a system for interactive platform access authorization is presented. The system may include a camera operable to obtain image data of a user. The system may include a processor in communication with the camera. The processor may be configured to receive the image data from the camera. The processor may be configured to extract facial features from the image data using a facial recognition process. The processor may be configured to determine an age estimation of a user based on the facial features. The processor may be configured to compare the age estimation to an age gate. The processor may be configured to authorize the user to engage with a video game based on the comparison.

The invention discloses a high-precision facial expression emotion detection method based on improved YOLOv8n, which optimizes a facial expression recognition task while retaining a basic framework of the YOLOv8n, and comprises the following steps of: firstly, replacing a standard convolution module in a backbone network and a neck structure of the YOLOv8n with a self-adaptive multi-branch convolution module; a large-scale separable nuclear attention module is integrated to the seventh layer and the tenth layer of a YOLOv8n backbone network, dynamic attention on key facial features is enhanced, interlayer feature injection connection is applied to the YOLOv8n backbone network and a neck structure, cross-layer connection between the sixth layer and the seventeenth layer and cross-layer connection between the ninth layer and the nineteenth layer are established, and therefore the dynamic attention on the key facial features is enhanced. By establishing the direct connection between the shallow and deep features, the information loss is reduced, the multi-scale feature fusion is optimized, and the precision and efficiency of facial expression emotion detection are significantly improved.

The invention discloses a hierarchical driving fatigue intervention method based on biological feature recognition, and relates to the technical field of intelligent driving safety monitoring, and the method comprises the steps: collecting a facial image of a driver through a vehicle-mounted multispectral camera system, extracting facial feature data, and carrying out the preprocessing of the facial feature data; constructing a fatigue recognition model based on the preprocessed facial feature data, respectively obtaining a visual fatigue score and a behavior fatigue score, and fusing the visual fatigue score and the behavior fatigue score to generate a comprehensive fatigue index; implementing a hierarchical intervention mechanism according to the comprehensive fatigue index, and continuously monitoring and updating the comprehensive fatigue index to determine whether to trigger a safety protection mechanism. According to the invention, accurate response and risk prevention and control of the fatigue state are realized through hierarchical intervention and a safety protection mechanism, and the driving safety guarantee capability and the user experience are remarkably improved.

This technology provides systems and methods for tracking facial movements and reconstructing facial expressions by learning skin deformation patterns and facial features. Frontal view images of a user making a variety of facial expressions are acquired to create a data training set for use in a machine-learning process. Head-mounted or neck-mounted wearable devices are equipped with one or more camera(s) or acoustic device(s) in communication with a data processing system. The cameras capture images of contours of the users face from either the cheekbone or the chin profile of the user. The acoustic devices transmit and receive signals to calculate a representation of the skin deformation. A data processing system uses the images, the profile of the contours, or skin deformation to track facial movement or to reconstruct facial expressions of the user based on the data training set.

The invention belongs to the technical field of image processing, and provides a facial paralysis grading method, system and equipment fused with multi-modal data and a medium in order to solve the problem that existing facial paralysis grading is inaccurate. Carrying out joint modeling on the handmade facial paralysis features based on prior knowledge and the depth visual features containing spatio-temporal information; extracting multi-scale features based on static image features and key point features extracted from a single-frame face image of a target individual, gradually transmitting small-scale features representing local asymmetry to medium-scale features and large-scale features, and adaptively performing feature fusion through dynamic weight distribution to obtain static symmetric features; and a bidirectional information interaction channel between the dynamic facial features and the static symmetric features is constructed, so that the generated fusion features simultaneously contain complete pathological information of spatial structure asymmetry and motion abnormality, and diagnosis grading is more accurate.

A method and system are disclosed for creating a graphic element representing encoded biometric data that represents the facial biometrics of two or more individuals. The method includes capturing facial images of each person, generating non-reversible face-derived feature vector data, and embedding this data into a graphic on the item. To validate that the graphic is derived from the people presenting the graphic, the graphic is processed to extract corresponding feature vector data, which is compared to the data derived from newly collected facial images of the presenting people. Validation indicators are based on threshold comparisons. The system supports verification using photographs on the item and enables secure association through trusted entities and cryptographic signatures. The graphic may encode alpha-numeric strings representing biometric and document data. The graphic may be applied to any document or item, including driver's licenses, passports, titles, ballots, and financial instruments.

The invention discloses a sign language synthesis service method based on multi-modality, and the method comprises the following steps: S10, carrying out end-cloud collaborative rendering setting: deploying an edge end as a lightweight model to generate a basic action, and deploying a cloud end as operating a MoMask and 3D rendering engine; s20, performing multi-modal data acquisition: acquiring a voice signal through a microphone, acquiring a 48 * 48 pixel face grayscale image through a camera, and acquiring action posture data through an IMU sensor; s30, performing emotion feature extraction on the collected multi-modal data: performing voice emotion recognition to output six types of emotion probability distributions, and performing facial expression recognition to output seven types of expression probability distributions; s40, performing cross-modal feature fusion: dynamically fusing the voice and facial features based on a confidence weighting strategy, and generating a three-dimensional emotion intensity vector; and S50, sign language action generation is carried out, and a 3D skeleton sequence matched with emotion is generated through RVQ layering quantification and MoMask Transformer.

Disclosed is a method for training an expression transfer model performed by a computer device, the method including: obtaining a source domain facial image of a first object, a target domain facial image of a second object and a facial feature image associated with the target domain facial image; applying the facial feature image and the source domain facial image to an expression transfer model to obtain a synthesized facial image of the first object; applying the synthesized facial image and the target domain facial image to a discriminative network model obtain two discrimination results; applying the synthesized facial image and the target domain facial image to an image classification model to obtain a category feature vector for identifying a difference between the synthesized facial image and the target domain facial image; and updating the expression transfer model according to the category feature vector and the discrimination results.

The invention belongs to the technical field of equipment screen angle adjustment, and aims to solve the problem of low efficiency of a method for rotating a computer screen by adjusting a mechanical bracket. The invention provides a method and a system for dynamically adjusting the posture of a notebook screen. The method comprises the following steps of: acquiring facial feature information of a user in front of the screen in real time; determining a current face rotation angle of the user based on position parameters of feature extraction points in the face feature information; obtaining an optimal angle range of a notebook computer screen based on the face rotation angle and a preset optimal vertical view field of a user; and controlling the notebook screen to perform angle adjustment based on the optimal angle range. The automatic rotation function of the screen can be achieved, the computer screen directly faces the face and eyes of a user in real time, the sitting posture of the user can be corrected, and the eyesight of the user is protected.

The invention discloses a fatigue driving detection method based on personalized feature fusion, and the method comprises the following steps: facial feature extraction and personalized dynamic adaptation: employing a facial detection algorithm to extract facial features of a driver; aiming at the facial features of different drivers, automatically adjusting the feature weight of each facial region through a personalized dynamic adaptation mechanism; performing spatial-temporal feature fusion and fatigue behavior pattern recognition: performing fusion through a multi-dimensional time sequence convolutional neural network to obtain a comprehensive fatigue recognition value; performing weighted fusion on the facial features, the driving behavior features and the vehicle state features to obtain final fatigue recognition output; and fatigue recognition output: integrating all the fused features and the optimized model, and outputting a final fatigue driving recognition result. According to the method, the facial feature weight is dynamically adjusted, spatial-temporal features and multi-source data fusion are combined, the adaptability and precision of the system can be improved, and it is ensured that the fatigue state is accurately detected under different drivers and driving environments.

The invention relates to the technical field of model construction, and provides an industrial manufacturing method based on a digital human basic model and related equipment thereof. The method comprises the following steps: performing target person facial region extraction on an input image to obtain a target region image, performing gender recognition on a target person to determine a basic model type, and performing facial feature point extraction on the target region image by using a coordinate recognition model to obtain a facial feature point coordinate set; then, the extracted facial feature point coordinates are mapped to the basic model in a feature matching mode to generate a first three-dimensional model, and in order to improve the details of the three-dimensional model, target detail enhancement is carried out on the first three-dimensional model according to a preset geometrical shape adjustment strategy to obtain a second three-dimensional model; and finally, performing structure optimization on the second three-dimensional model according to a preset model optimization strategy to generate a third three-dimensional model. According to the method, facial feature extraction and matching and model optimization are automatically carried out, so that the production efficiency and quality of the digital human are improved.

The present disclosure generally relates to displaying visual effects such as virtual avatars. An electronic device having a camera and a display apparatus displays a virtual avatar that changes appearance in response to changes in a face in a field of view of the camera. In response to detecting changes in one or more physical features of the face in the field of view of the camera, the electronic device modifies one or more features of the virtual avatar.

The invention relates to the technical field of sound source localization, and provides a sound source localization method and system based on fusion confidence. The method comprises the following steps: when a preset wake-up word is detected, starting sound source positioning processing, calculating a horizontal azimuth angle according to a detected current voice signal, and determining a sound source positioning area; calculating a matched existing user voiceprint, and confirming a current user corresponding to the current voice signal; calculating a user preference fusion coefficient corresponding to the current voice signal, calculating a fusion confidence coefficient of the current voice signal, dynamically adjusting a scanning range of a holder camera, capturing an image frame of a current user at a fixed interval in a scanning process, executing face detection, obtaining a to-be-processed face image, performing face feature extraction, and obtaining a to-be-processed face image; and carrying out visual identity collaborative confirmation, stopping rotation of the holder camera when an identity consistency condition is satisfied, and locking the current direction as a sound source positioning position. According to the invention, the hardware complexity is reduced, and the positioning precision is effectively improved.

The invention provides an immersive projection man-machine interaction method and system, and the method comprises the steps: constructing a closed projection space which comprises a plurality of walls, a bottom surface and a top, deploying a main image collection device at the center of the top, achieving the positioning of the head space of a user, and arranging auxiliary image collection devices at a plurality of corners of the combination part of the adjacent walls. Based on the real-time image data acquired by the main device, the intelligent recognition technology is adopted to analyze the three-dimensional coordinates of the center of the head circle of the user, the face orientation angle is calculated in combination with the facial feature data acquired by the auxiliary device, and the dynamic mapping model of the user interaction intention is determined by fusing the space coordinates and the orientation angle. According to the method, a main-auxiliary cooperative visual positioning framework is adopted, space perception limitation under a single visual angle is avoided, two-dimensional real-time tracking of a user position and a sight line direction is realized, an accurately matched interactive animation is generated at a corresponding position of a projection space according to a dynamic analysis result, and the user experience is improved. And the real-time response capability and behavior intention capture accuracy of human-computer interaction in an immersive environment are improved.

The invention discloses a digital signature generation and authentication system and method. The system comprises a first terminal and a second terminal, the first terminal comprises a first acquisition module for generating a first graphical interface, responding to a signature executed by a user on the first graphical interface, and acquiring first vector coordinate data signed on the first graphical interface; the second acquisition module is used for sampling facial features of the user; the data processing module is used for extracting a first secret key parameter from the first vector coordinate data, taking the facial feature as a second secret key parameter, and encrypting the first vector coordinate data through the first secret key parameter and the second secret key parameter to obtain secret key data; the storage module is used for storing key data; the communication module is used for transmitting the key data to a second terminal; the second terminal comprises a third acquisition module for verifying the facial features of the user; and the generation module verifies the facial features, decrypts the key data by using the second key parameter, and requests the first terminal to generate a digital copy to the second graphical interface.

Techniques are disclosed for automatically activating, applying, and / or otherwise achieving a location-specific makeup look to a person. Based on detecting that the person has physically entering into a bounded location, a location-specific makeup look may be automatically triggered to be activated and applied to (or otherwise achieved on) the person by controlling one or more smart cosmetics apparatus(es). A location may be bounded by geospatial coordinates, date, day, time of day, occasion or event, and / or other desired parameters. The activated location-specific makeup look may be automatically customized and / or manually modified for the person based on the person's facial features, skin type, personal preferences, environmental characteristics of the physical location, an occasion and / or event corresponding to the bounded location, and / or characteristics of the cosmetic products that are applied to the person to achieve the location-specific makeup look.