873 results about "Face perception" patented technology

The invention belongs to the technical field of face recognition, and particularly relates to a face recognition algorithm adaptive to illumination variation, which uses Gaussian filtering to reduce noise caused by illumination variation, convert a color image into a grey-scale image, reduce calculation complexity, perform adaptive histogram equalization, estimate global illumination conditions in the image and improve the face recognition accuracy. Calculating the main direction and intensity of illumination in the image, predicting the current illumination condition, and enabling the image brightness to adapt to different environment illumination; identifying a face region in the image, using a deep learning method to position face related points, using an LBP to extract local features insensitive to illumination variation, and using a deep convolutional neural network to extract global features; introducing a multi-illumination data set; the Euclidean distance is used for comparing the extracted feature vectors, the similarity between the extracted feature vectors and known faces in a database is recognized, according to the similarity score, threshold judgment is adopted for AQW to obtain a final recognition decision, and the method has the effect of being capable of accurately adapting to facial features under different illumination conditions in real time.

The invention discloses a face recognition system based on computer vision and an application method thereof, and relates to the technical field of face recognition. The system comprises a fine-grained shielding classification module, a multi-modal feature fusion module, a cross-modal confrontation identification module and a compensation verification module. By analyzing the depth data and the polarized light parameters, an occlusion thermodynamic diagram is generated, and an identifiable area and an occlusion area are marked. And according to the occlusion label of the thermodynamic diagram, activating infrared and sonic sensor data, extracting the relevance between the temperature change and the vibration frequency, and generating a facial occlusion area feature map. And calculating the similarity between the sheltered region feature map and the non-sheltered region feature, and detecting a forgery attack. And analyzing the difference rate of adjacent frames through a sliding window, marking compensation distortion, rejecting low-credibility data in combination with confidence and attack marks, and outputting a complete facial feature vector and a risk level. And the recognition accuracy and the system security in a complex environment are remarkably improved.

The invention belongs to the technical field of face recognition, and particularly relates to a multi-modal face recognition method and system based on deep learning, and the method comprises the steps: carrying out the real-time collection of a target through employing sensors of different modals, and carrying out the denoising, graying, image cutting and alignment processing of a collected image; extracting features in the RGB image by using a convolutional neural network, and extracting heat map features from the infrared image by using a CNN structure; fusing the data of different modalities at an input layer of the network, merging the data into a multi-modal input for feature extraction, after features are independently extracted from each modal, splicing or performing weighted summation to merge the modalities, and classifying the modalities through a full connection layer; a deep neural network is used for training, deep semantic information is learned from the fused multi-modal features, a proper loss function is selected according to a task target, and fine tuning optimization is carried out according to a data set; the full connection layer or the SVM is adopted for final category prediction, a face recognition result is output, and the effect of efficiently recognizing the face is achieved.

The invention provides a face image recognition method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the fusion of a topological relation feature vector and a biological feature contour, calculating the curvature manifold of the concave region of five sense organs, and generating a local curvature feature vector through differential geometric transformation; inputting the topological relation feature vector, the local curvature feature vector and the micro feature coding sequence into an evolvable biological feature library, and fusing a space-time weight to update a comparison template; fusing the micro-feature coding sequence, the topological relation feature vector and the local curvature feature vector to obtain a biological feature set, and calculating the multi-scale similarity between the biological feature set and the updated comparison template; and if the multi-scale similarity exceeds an adaptive threshold, generating a face recognition result. According to the invention, the stability and accuracy of face recognition in a complex illumination environment are improved.

The invention discloses a multi-modal dialogue emotion recognition method, and relates to the technical field of multi-modal emotion recognition and human-computer interaction, and the method comprises the steps: obtaining an expression time sequence feature and a posture time sequence feature through a face recognition model and a posture recognition tool; the attention module performs adaptive weighted fusion on the expression and posture features to obtain fused visual modal features; constructing a new expression of the context information, obtaining an emotional expression based on a prompted emotional modeling technology, and extracting a text modal feature through a text encoder; using the data vectorization model to extract modal features of the voice corresponding to the speaker; a jump connection multi-head attention cross-modal fusion method is provided, cross-modal alignment and fusion are carried out on multi-modal modal features, and then emotion recognition is carried out through an emotion classifier module. According to the method, the problems of insufficient key emotion clue recognition and insufficient fusion in traditional multi-modal emotion recognition are effectively solved, and the accuracy and robustness of emotion recognition are improved.

The invention discloses a face feature decoupling representation method and device with causal effect transmission and a medium, and relates to the technical field of causal discovery, image generation and decoupling representation learning. The method comprises the following steps: establishing a variational auto-encoder, and in response to input face data, learning from a potential space based on the variational auto-encoder to obtain potential feature distribution; integrating a structural causal model into the variational auto-encoder, and modeling a causal effect to extract causal features from the potential feature distribution; establishing a graph attention network, inputting the causal features into the graph attention network, and transmitting a causal effect during decoupling; and designing a loss function, and training the graph attention network by using a discriminator. The method can effectively mine and transmit the causal relationship between the face features, improves the explanatory and transferability of feature representation, has high application potential, and is especially suitable for tasks such as face recognition and expression analysis.

The invention relates to a method and a system for realizing personalized recommendation based on multi-feature fusion face recognition, and aims to provide real-time, efficient and accurate recommendation service, the method comprises the following steps: collecting a face image of a target audience in real time through a camera device, and preprocessing the image to extract a face region; running a face recognition algorithm in the edge computing device, and extracting multi-modal features including age, gender, appearance and style features; a comprehensive feature vector is generated by using a Transform model through a feature fusion module, and style classification and appearance classification are realized; according to the method, user feature vectors and a content database are matched on the basis of cosine similarity, personalized recommendation content is generated, the weight of a classification model is dynamically optimized in combination with user feedback, and dynamic behaviors and appearance features of a user are accurately captured through a time sequence analysis module, a dynamic style analysis module and a deep appearance analysis module; and comprehensively generating user feature description by combining vision, voice and character multi-mode information.

The invention discloses a double face recognition area early warning safety protection method, and relates to the technical field of face recognition, a face recognition camera, a database, a background server and a client are included, and the method has the advantages that under strong light, an intelligent light adjustment algorithm can automatically reduce the exposure, improve the contrast and enhance the color saturation; the face features are highlighted, the overexposure phenomenon of the face is avoided, under the backlight condition, the backlight compensation algorithm is started, the overall brightness of the face is improved by increasing the edge brightness and adjusting the hue, the accuracy of feature extraction is ensured, and in addition, based on the face detection technology of the deep neural network, through deep training of massive face image data, the accuracy of feature extraction is improved. According to the invention, the camera can learn the face features in various complex environments, and even if the face is partially shielded and is located at different angles or under poor light conditions, the system can accurately carry out detection and recognition, so that the stability and accuracy of face recognition are greatly improved.

The invention provides a screen interaction method and system based on face recognition, and the method comprises the steps: obtaining a face image sequence triggered by a target user in a screen interaction scene, carrying out the matching of a target interaction mode from a preset interaction mode library according to the face dynamic behavior characteristics, and carrying out the matching of a target interaction mode based on the coordinates of a screen fixation region and the target interaction mode. Generating dynamic interaction content corresponding to the sight focus of the user, calling a pre-trained facial feature fusion model, performing multi-level feature extraction on a facial region in the facial image sequence, generating a user identity verification vector and an emotional state vector, and performing real-time adjustment on presentation parameters of the dynamic interaction content based on the multi-level feature extraction. And outputting the adjusted screen interaction signal. According to the invention, the user experience can be optimized while the interaction efficiency is improved.

The invention discloses an access control face recognition system and method based on a three-dimensional technology, and relates to the technical field of access control face recognition, and the recognition system comprises a face data acquisition module which is used for capturing three-dimensional face data of a target person by using a 3D camera; the multi-modal fusion module is used for fusing the three-dimensional face data and the traditional two-dimensional image data; the modeling module is used for generating an accurate three-dimensional face model; the dynamic adjustment module is used for creating a real-time dynamic adjustment model; the matching module is used for extracting facial features from the three-dimensional face model and matching the facial features with the three-dimensional face model in the database; and the identity verification and authorization module is used for carrying out identity verification according to the matching result. According to the method, three-dimensional face data and traditional two-dimensional image data are fused, the recognition accuracy and reliability can be improved, a real-time dynamic adjustment function is introduced, recognition parameters can be automatically optimized according to different environment light rays and angles, and better use prospects are brought.

The invention relates to a tracking-based clustering face recognition method and device. The method comprises the following steps: acquiring a video stream with a face image; face tracking is carried out on face images in the video stream, clustering is carried out on the face images according to a tracking trajectory, and face IDs and face features corresponding to all clusters are obtained; and recognizing the target object according to the tracking trajectory and the face features, and determining the face ID of the target object. The face data in the video stream is processed through face tracking and clustering, preparation work such as early-stage high-precision face data collection is not needed, meanwhile, face data collection and face recognition work are both based on the video stream format, the matching degree is improved, and therefore face recognition is more accurate.

The invention provides a digital human real-time question-answering method, system and device for intelligent emotion interaction and a medium, and the method comprises the steps: capturing identity information, facial expressions and the like of different figures through face recognition and face attribute analysis, and obtaining an instruction and emotion of a user based on voice recognition and voice emotion analysis; information such as instructions, identities and emotions of a user is fused through a large language model to output appropriate answers, multi-round question answering can be conducted on associated question association contexts, meanwhile, appropriate emotions are given to the answers of the large model in combination with context analysis and emotion analysis, and what expressions and actions are inserted at a certain time are calculated; performing speech synthesis by using proper emotion, and realizing matching of audio and digital population types by using a mouth shape generation algorithm; and finally, generating expressions and actions at proper opportunities in combination with contexts and emotions while broadcasting the voice of the digital person. According to the method, high-simulation digital human real-time question answering with high intelligent emotion interaction can be realized.

The invention provides a face recognition method and device, electronic equipment and a medium, and the method comprises the steps: processing a scaled to-be-recognized face image through a face detection model, and determining the coordinates of a face region and a plurality of key points in the original to-be-recognized face image based on the recognition result of the face detection model; based on the coordinates of the plurality of key points in the to-be-recognized face image and the coordinates of the plurality of key points in a preset face template, aligning a face region in the to-be-recognized face image to the preset face template, and outputting an aligned face image of a preset size; processing the aligned face image through a face recognition model comprising a down-sampling module, and generating a target face feature of the face image to be recognized; the user information of the target user corresponding to the to-be-recognized face image is determined based on the similarity between the target face feature of the to-be-recognized face image and the feature vector of the user in the database, so that the calculation amount in the face recognition process is reduced, and the accuracy and precision are both considered.

The invention discloses an emotion recognition method based on a global feature and local feature fusion network, and relates to the technical field of face recognition. Comprising the following steps: constructing an emotion recognition model based on a global feature and local feature fusion network GLFNet; inputting a face image to be recognized into the emotion recognition model, extracting an intermediate feature map of the face image through the backbone network ResNet-18, and respectively sending the intermediate feature map into the global module and the local module to obtain a global feature map and a local feature map; the feature fusion module fuses the global feature map and the local feature map to obtain fused features; residual connection is established between the intermediate feature and the fusion feature to obtain a final fusion feature; and performing full connection layer FC processing on the final fusion feature to obtain an emotion recognition result. According to the method, the accuracy of a facial image emotion recognition result can be improved, richer fusion feature information is obtained, and higher performance is provided for a facial expression recognition task.

The invention discloses a clean tag backdoor attack method based on face variable features. The method comprises the steps of obtaining a face recognition data set D1, a sub-data set D2 and a classification neural network; using the D1 to train a classification neural network to obtain a face recognition model; generating a pair of confrontation samples for each clean sample in the sub-data set D2; a backdoor trigger is embedded in the confrontation sample to generate a poisoning sample, the category label of the poisoning sample is the category label of the clean sample corresponding to the poisoning sample, and all the poisoning samples form a poisoning data set D3; and combining D1 and D3 to train a face recognition model to obtain a back door model, and generating a to-be-attacked poisoning sample and attacking the back door model by using any face image. According to the method, the adversarial disturbance and face attribute editing technology is combined, the method does not depend on changing labels of training samples, the back door is implanted by modifying the hair style and the hair color, the concealment of back door attack can be improved, the learning of the model on trigger features can be enhanced, and the attack effectiveness is improved.

The invention discloses a face recognition method based on sparse coding and dictionary learning. The method comprises the following steps: S1, collecting and preprocessing a standardized training image and a to-be-recognized face image; s2, training a dictionary set, labeling categories, and initializing and optimizing the dictionary; s3, performing sparse coding, and solving a sparse coefficient by using the optimization dictionary; s4, carrying out regional feature division and fusion of multi-region sparse coding, weighted compression and interactive modeling; s5, performing fusion feature normalization and classification, and performing joint discrimination by a reconstruction error and a discrimination classifier; s6, performing confidence evaluation and dictionary updating, and triggering an incremental learning updating mechanism with low confidence; and S7, performing model deployment and real-time identification, and deploying an optimization dictionary and a classification module to perform identification output. The invention provides a face recognition method suitable for a complex environment, and the method has the advantages of high precision, strong robustness and good practicability.

The invention belongs to the technical field of image data processing, and discloses a driver fatigue state early warning method and system based on face recognition tracking, and the method comprises the steps: extracting a face region through semantic segmentation, splitting a single-channel gray-scale map, calculating the local complexity and noise degree of a pixel point, obtaining a window adjustment coefficient, and carrying out the adjustment of the window adjustment coefficient; the size of the adaptive window is determined, different pixel region features can be adapted in a targeted manner, and the processing deviation caused by a fixed window is effectively reduced; an illumination component is subsequently extracted based on an adaptive window, and the image is enhanced through a Retinex algorithm, so that the face image quality under complex illumination can be improved, and the detail definition is improved; and finally, the fatigue degree is detected in combination with a PERCLOS algorithm, so that the facial fatigue characteristics of the driver can be more accurately captured, and effective recognition and early warning of the fatigue state are realized. According to the invention, continuous and stable monitoring can be realized in the driving process, reliable guarantee is provided for driving safety of a driver, and the risk of accidents caused by fatigue driving is reduced.

The invention discloses a face recognition method and system based on cross-age features, belongs to the technical field of face recognition, and is used for solving the problems that traditional face recognition is slow in old people who need to see a doctor through face recognition, slow recognition and recognition errors are easily caused for people with large age spans, and the face recognition efficiency is high. And the medical seeing efficiency of the user is influenced. The method comprises the following steps: performing marking processing on cortical layer texture features and variable epidermal layer features on an acquired initial facial image, and determining a facial cortex feature image; carrying out dynamic stripping processing on variable epidermal layer features in the facial cortex feature image, and carrying out physical optical constraint on dermal layer texture features; carrying out extraction processing on facial feature points under related topological structures and shapes on the optimized facial image; performing age attribute editing diffusion processing on the optimized face image; and performing feature dimension reduction processing on the face key features, and determining social security identity information of the medical seeking personnel.

The invention relates to the technical field of face recognition, and particularly discloses a high-precision face recognition method and system based on transfer learning, and the method comprises the steps: S1, selecting a plurality of universal deep learning models, and carrying out the pre-training through a universal face data set; s2, expanding the data volume by adopting a deep data enhancement technology, and introducing a generative adversarial network to carry out data enhancement; establishing an annotation optimization system; s3, performing multi-dimensional scene feature extraction on the acquired face image, performing weighted fusion on scene features of different dimensions by adopting an attention mechanism, designing an adaptive model structure, and dynamically adjusting parameters and structures of the model according to different scene features; s4, performing joint training on a plurality of models of different architectures by using the face data fused with the scene features and the enhanced face data; and iteratively optimizing the model regularly. According to the method, the accuracy and consistency of labeling are improved, so that the model can learn more accurate face features, and the recognition precision is improved.

The invention relates to the technical field of face recognition, and discloses a face recognition method and system suitable for an outdoor electronic instrument to solve the problem of low recognition precision under complex illumination, posture change and shielding conditions in the prior art, and the method comprises the steps: S1, collecting image data, and carrying out the preprocessing; s2, performing face region detection and face key point positioning on the illumination enhanced image; s3, performing affine transformation based on the face detection result set to perform face alignment; s4, extracting a face feature vector based on a ResNet100 structure; s5, performing attitude-guided feature reconstruction processing on the face feature vector to obtain an attitude-enhanced face feature vector; s6, performing feature normalization and identity classification on the posture-enhanced face feature vector, and outputting a face recognition result; and S7, outputting a final identity recognition result and confidence. According to the method, an efficient and scientific optimization scheme can be provided in outdoor face recognition, and remarkable technical values and economic benefits are brought to practical application.

The invention discloses a sperm library identity verification system and method based on face recognition, belongs to the technical field of information management, and solves the problems that a three-dimensional face recognition device of an existing system cannot complete identity verification at different angles and postures, and the recognition speed of the system and the waiting time of a user are delayed. The method comprises the steps that facial feature information is collected in real time, an identity recognition model is executed, and the identity recognition model recognizes and analyzes the facial feature information with sperm library associated personnel information as prior information; iterative training is carried out on the identity recognition model through the training set and the test set, the identity recognition model recognizes and analyzes the facial feature information by taking the sperm library associated personnel information as prior information, and the verification accuracy is not influenced by taking the sperm library associated personnel information as the prior information. And feature attention analysis is carried out on the facial feature information under the multi-view attitude, so that the identity verification accuracy of the sperm bank is improved, and the model calculation amount is reduced at the same time.

The invention discloses a coal mine underground safety detection method and system based on face image recognition, and relates to the technical field of coal mine safety detection. The method comprises the following specific implementation steps: through a video target recognition technology and a self-adaptive face recognition method, recognizing the identity of a miner and the wearing condition of safety equipment in each working area, and obtaining a miner number and a first danger index; secondly, according to the motion trail matching and behavior analysis technology, analyzing the danger degree of various behaviors of the miner to obtain a second danger index; then, according to the environmental parameters, evaluating the danger level of the miner working environment, and calculating a third danger index; finally, in combination with the danger index of the miner and the danger degree of the working area, the working safety condition of the miner is comprehensively evaluated, the comprehensive danger index is obtained, danger grading is carried out, early warning information can be sent out in time, the safety risk of underground operation of the coal mine can be effectively reduced, the safety of operators is ensured, and safety accidents are reduced.

The invention discloses an intelligent accompanying robot system based on multi-modal interaction and a control method, which adopt a dual-chip collaborative framework to realize multi-modal emotion interaction. According to the equipment, a quantized and optimized lightweight face recognition and voice detection model is locally operated in an off-line manner through an ESP32-S3 chip, and facial expressions and voice emotions of a user are analyzed in real time; meanwhile, an STM32F103RCT6 chip is adopted to be responsible for peripheral control, and the peripheral control comprises OLED expression display, body movement driven by a servo motor and a music feedback module, so that visual and dynamic emotion feedback is achieved. A task polling mechanism is utilized, face and voice recognition tasks are efficiently and alternately processed under limited computing resources, and low power consumption and high responsiveness of the system are ensured. The interactive naturalness and the user immersion of the intelligent toy are improved, and the intelligent toy has a good personalized emotion regulation effect and is suitable for the fields of child accompanying, psychological counseling, special crowd assistance and the like.

The invention discloses an emotion rehabilitation intervention method based on face recognition. The method comprises the steps that S1, a user face image sequence is collected through a front camera; s2, performing image preprocessing to generate a standardized image tensor set; s3, facial dynamic features are extracted through an improved BiFormer network, emotional states are recognized, and a multi-dimensional emotional state sequence and a facial expression coding sequence are generated; s4, jointly analyzing the emotion change trend and the expression behavior mode of the user through a multi-mode RETNet network, and outputting a psychological state grade and a future emotion fluctuation score sequence of the user; s5, matching an emotion rehabilitation intervention strategy through a preset rule engine; s6, executing the emotion rehabilitation intervention strategy, collecting user data in real time, and generating a user response scoring sequence; and S7, performing incremental updating on the multi-modal RETNet network and a preset rule engine based on the user response score sequence. According to the invention, the accuracy of emotional state recognition and the intelligent level of intervention strategy pushing are improved.

The invention discloses an OSA judgment method based on face and neck contour image features, and relates to the technical field of OSA judgment. Comprising the following steps: acquiring a face and neck contour image of a to-be-detected individual through an image acquisition module, extracting face and neck contour feature values of an OSA risk from the face and neck contour image based on a feature extraction module, analyzing the eight face and neck contour feature values through a risk assessment module, and judging the risk level of the to-be-detected individual suffering from the OSA, the method has the advantages that the anatomical features, namely the palatopharyngeal length and the mandibular body length, which are most related to the OSA severity are converted into the human body external features, diagnosis can be performed by photographing the human body appearance image, the requirement on equipment is low, the diagnosis process is simplified, and the diagnosis accuracy is improved by combining the edge and skin color information. The face recognition, the region segmentation and the proportion measurement can be efficiently realized, the most convenient and accurate measurement is realized, and a step-by-step judgment mode is adopted, so that the accuracy is improved, the updating logic is optimized, and the subsequent maintenance cost is reduced.

The invention discloses a face recognition method based on multi-modal fusion, and belongs to the technical field of face recognition, in the face recognition method, when visual information is lost due to face partial shielding or strong light irradiation, emotion features in voice signals can automatically compensate for the deficiency of visual information. For example, in a video call scene, when a user shields a chin area with a hand, the system can still accurately recognize anxiety emotion by analyzing intonation trembling characteristics in voice and combining muscle movement of an unshielded eyebrow and eye area, and misjudgment caused by local information loss is avoided. Meanwhile, under the condition that noisy background sound interferes voice, the model can effectively filter interference of voice noise and maintain accuracy of emotion judgment by capturing continuous duration and intensity changes of facial micro-expressions, such as slight twitching of mouth corners lasting for more than 3 seconds.

The invention relates to an occluded face recognition method based on FaceNet and an attention mechanism. The method comprises the steps of data set preprocessing, face recognition network construction, network training and testing. According to the method, an MTCNN backbone network is adopted to carry out face region detection and face key point detection, an attention mechanism is used to focus the network on an unshielded part, a CNN neural network is adopted to map a face image to a 128-dimensional Euclidean space, and the similarity degree of two persons is judged according to the Euclidean distance between two portraits. The invention aims to solve the problem of how to obtain the face features of the non-shielding face part, so that the performance and robustness of a face recognition task are improved, and the face detection effect in a complex shielding scene is improved.

The invention discloses a face recognition-based beauty and makeup recommendation method, device and equipment and a medium, and relates to the technical field of artificial intelligence, and the face recognition-based beauty and makeup recommendation method comprises the steps of obtaining face information of a target user; the face information of the target user is input into a beauty makeup recommendation model, a face analysis result and beauty makeup suggestion information of the target user are obtained, and the beauty makeup suggestion information comprises recommended makeup and a weight value corresponding to the recommended makeup; performing preference scoring on the recommended makeup according to the face analysis result through a collaborative filtering recommendation algorithm to obtain a predicted preference scoring result of the target user on the recommended makeup; the weight value of the recommended makeup is adjusted according to the predicted preference degree scoring result in combination with the relation structure in the beauty makeup knowledge graph, the final recommended makeup for the target user is obtained, and the obtained final recommended makeup for the target user can improve the precision of beauty makeup recommendation.

The invention relates to the technical field of image recognition, and discloses a training method of a face feature extraction model, and a face feature extraction method and device. The training method comprises the following steps: acquiring multiple groups of training face images; inputting the training face image into a preset to-be-trained model to perform angle compensation and feature extraction on the training face image to obtain predicted face features; training a to-be-trained model according to the predicted face features and the corresponding model loss information to obtain a face feature extraction model; the model loss information is obtained by fitting angle invariance loss information, metric learning loss information and angle sensitivity loss information of the predicted face features. According to the embodiment of the invention, angle compensation can be performed on the to-be-recognized face, so that the face recognition precision is improved.

The invention provides a three-dimensional tracking scanner with a face recognition safety system and a control method. The three-dimensional tracking type scanner comprises a scanner body; the face recognition module is connected with the scanner body and comprises a binocular camera assembly which is used for responding to the situation that an object approaches the three-dimensional tracking type scanner and synchronously collecting left and right images of the object; the binocular reconstruction unit is used for establishing a three-dimensional grid model of a human face according to the left and right images when the detected object is the human face; the living body detection unit is used for carrying out living body detection on the human face by adopting a multiple detection mechanism based on the three-dimensional grid model; the face recognition unit is used for performing identity recognition on the user corresponding to the face under the condition that the living body detection is passed; and the control management system is connected with the face recognition module, and the control management system is used for allowing the user to execute an operation matched with the permission of the target identity on the scanner body under the condition that the identity of the user is the target identity.