446 results about "Face perception" patented technology

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 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 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 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 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 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 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.

The invention provides a coding and decoding three-dimensional face recognition method and system based on frequency feature assistance, and belongs to the technical field of face recognition. And processing the acquired to-be-recognized face image by using a pre-trained recognition model to obtain a three-dimensional face depth map. According to the method, a coding and decoding network is utilized, a multi-scale encoder structure is adopted, hierarchical features of a face depth map are extracted in a gradual down-sampling process, and key geometric information including skin textures and five sense organ contours is effectively reserved; in the decoding stage, the network selectively fuses features of different levels through jump connection and an attention mechanism, so that redundancy is remarkably reduced, and the efficiency of the face depth map is improved; dynamically selecting the frequency characteristics by adopting an attention mechanism, and selecting the frequency characteristics with relatively large information amount so as to enhance the denoising capability; and coding features and decoding features are effectively fused, so that the feature refining effect is improved.

The invention relates to the technical field of intelligent security and protection, in particular to a face recognition access control method and system in an intelligent security and protection system. The method comprises the following steps: sensing an illumination condition through an ambient light sensor and adaptively selecting an image enhancement algorithm to pre-process a face image; after features are extracted, similarity comparison with a database template is carried out; meanwhile, multi-mode living body identification integrating action instructions and near-infrared detection is carried out; and the multi-dimensional access authority of the user is dynamically inquired and verified in the identification process. The system comprises an image acquisition module, an environment perception module, a central processing module, a living body detection module, a dynamic authority management module, an access control module and the like. According to the invention, the recognition rate under complex illumination is effectively improved, the anti-counterfeiting capability of the system is enhanced, and the intelligent dynamic management of the access authority is realized.

The invention provides an electrical stimulation task processing method and device and terminal equipment, and the method comprises the steps: inputting a collected user face image into a pre-trained face detection model for face recognition, and obtaining a face region recognition result; inputting the face region recognition result into a pre-trained key point recognition model for key point detection to obtain a face key point recognition result; constructing a face feature coordinate system based on the face key point recognition result, and mapping the face acupoints to the face feature coordinate system in combination with a preset mapping rule to obtain a user face acupoint positioning result; performing acupoint impedance detection according to a user face acupoint positioning result, calling a corresponding electrical stimulation waveform according to an electrical stimulation task initiated by the user when the acupoint impedance detection result is passed, and executing the electrical stimulation task based on the electrical stimulation waveform. According to the invention, the alignment accuracy and the treatment effectiveness of the TENS equipment in the use process can be improved.

The invention relates to the technical field of face recognition, in particular to a cross-age face recognition method and system fusing 3D point cloud and deep learning. The method comprises the steps of extracting 3D point cloud data of a face image based on the face image, performing preliminary correction processing on the face based on the 3D point cloud data to obtain a face target point set, and performing fine registration and alignment on the face target point set based on an ICP algorithm; three-dimensional face representation is extracted based on the face target point set after fine registration and alignment; and performing contrast calculation on the three-dimensional face representation and the preset three-dimensional face representation stored in the feature access stack to obtain a contrast, and generating a face recognition result based on the contrast, thereby improving the accuracy of face recognition.

The invention relates to the technical field of face recognition, and discloses a multi-modal alignment training-based face sentiment analysis method and system, and the method comprises the steps: obtaining multi-source data, carrying out the preprocessing of the multi-source data, obtaining a training set, constructing a basic model for the face sentiment analysis, selecting a sample with an inference text to generate a small number of high-quality sentiment analysis samples, and carrying out the recognition of the high-quality sentiment analysis samples; supervising and finely adjusting the model; selecting a sample with a facial action unit label and an emotion label, and performing reinforcement learning training on the model in combination with the predicted accuracy of the facial action unit label, the emotion label and the reasoning text; and training the basic model by using the training set, expanding the original training set by using the output of the trained basic model to obtain a new training set, continuously training the model, stopping training until a preset condition is met to obtain a final basic model, and performing facial sentiment analysis by using the final basic model. According to the method, illusion can be controlled, the accuracy of a facial emotion analysis result is improved, and the data set construction cost is reduced.

The invention belongs to the field of smart park visual analysis, and particularly relates to a smart park visual analysis system based on multi-model collaborative reasoning and dynamic request management, which is composed of a multi-model collaborative reasoning architecture and a dynamic request management and lightweight data flow design module. Through deep cooperation of the three modules, full-process optimization from target detection, face recognition to service deployment is realized, accurate recognition and real-time monitoring of people, vehicles and areas in a park are realized, and multi-target intelligent perception is realized. According to the method, a complicated visual analysis task is disassembled into a plurality of basic and independent atomic power modules by decomposing and designing an atomic power recognition process. Atomic energy is constructed on the basis of a YOLOv7 target detection model, a PPOCR license plate recognition model and an AdaFaceNet face recognition model, and the atomic energy is responsible for detecting targets such as people, vehicles and objects in images, recognizing license plates and analyzing facial features of people.

The invention provides an access control face recognition method and device based on a laboratory Lims system, relates to the field of intelligent recognition, and solves the problems that the face recognition technology in the prior art depends on light intensity and no shielding, when face recognition is carried out in some dark environments or specific occasions, multiple times of recognition and authentication are possibly needed, and the face recognition efficiency is high. And certain inconvenience exists in the prior art. The method comprises the following steps: acquiring a face image through a camera; the face image is preprocessed; adjusting the illumination intensity of the preprocessed face image; recognizing a shielding area in the face image; constructing the face image according to the occlusion area; analyzing the similarity between the constructed face image and a plurality of storage images in a face database; and identifying the identity of the face image based on the similarity threshold. The method and the device are used in the access control face recognition process.

The invention relates to the technical field of computer vision, in particular to a face recognition intelligent footpath terminal system applied to a scenic spot, which comprises a face image acquisition module, a feature point positioning module, a vertical motion analysis module, an energy consumption judgment module and a data communication module. According to the method, a time sequence vertical velocity difference and vertical kinetic energy quantification model is constructed through the displacement of the eyebrow arch key points in the continuous image, and movement intensity periodic data is formed by continuous displacement change, acceleration amplitude and duration, so that the local fine movement of the face is stably analyzed; dynamic behavior characteristics are kept in differential expression in illumination fluctuation and attitude change environments, so that the system obtains higher dynamic judgment consistency in a complex footpath scene, the accuracy and continuity of passenger flow recognition and behavior statistics are enhanced, uploaded data have clearer motion quantification significance and reliability, and the system is more suitable for popularization and application. And the problems of statistical offset and judgment lag caused by dynamic environment interference are improved.

The invention provides a face recognition access control management method and system for a smart community. The method comprises the following steps: constructing a face illumination quality evaluation model to recognize an abnormal area, performing repair by adopting a multi-stage compensation mechanism, and constructing a face feature density field to recognize a sensitive area; dynamically calculating the grid size of each area of the face; extracting texture, shape and semantic features of various scales in the re-divided grids, and calculating a feature contribution value of each grid unit; and constructing a multi-modal input tensor, inputting the multi-modal input tensor to the multi-branch convolutional neural network for training, establishing a closed-loop updating mechanism based on data generated during operation of the access control system, and continuously optimizing the convolutional neural network and parameters of the previous steps. According to the method, the output quality is ensured through multi-dimensional verification, including feature point spatial distribution rationality inspection, density field continuity verification and sensitive area and facial anatomical structure consistency inspection, and the accuracy and reliability of an identification result are effectively guaranteed.

The invention relates to the technical field of face recognition, and discloses a face recognition-based personnel identity verification and management system, which comprises a neural network image library construction module, a face image adaptive extraction module, an image living body detection and evaluation module, a neural network image library verification module and an early warning mechanism protection module, the method comprises the following steps: compressing images in an image library through a neural network, collecting face images of visitors in real time, carrying out complex condition processing on the face images of the visitors to obtain face contour information of the visitors, and carrying out gradient feature fusion to obtain face contour information after fusion of multiple frames of images; based on the three-dimensional depth information of the face image of the visitors, image living body detection and evaluation are performed on the face image of the visitors, identity verification and management of the visitors are completed, and the security of identity verification of the visitors is improved by accurately extracting facial features.

The invention provides a distributed face feature real-time extraction method and system based on edge calculation, and relates to the technical field of face recognition, and the method comprises the steps: carrying out the time sequence sampling of a collected face image, obtaining a multi-frame sequence, constructing a spatial-temporal feature association graph, and predicting and correcting a face position, dividing the optimized image sequence into task packets, distributing the task packets to edge computing equipment, constructing a feature point local tensor field, extracting feature description information, and finally selecting an optimal feature fusion matrix. According to the invention, the real-time performance and accuracy of face feature extraction are improved, and the consumption of computing resources is reduced.

The invention relates to the technical field of security and protection monitoring, in particular to an AI-based face recognition security and protection method, which comprises the following steps: recognizing a channel based on monitoring an entrance and exit, collecting and analyzing a multispectral face image, screening feature points and skin color areas, optimizing spatial feature matching and structural distribution, and fusing head actions and face behavior parameters. And judging structural consistency and linkage features, and outputting a face recognition mapping result. According to the method, facial detail level discrimination is realized through multi-spectral information fusion, parallel matching of continuous spatial features and time sequence behavior parameters and synchronous screening of behavior linkage and facial structure features are carried out based on regional pixel distribution and a structure sequence in combination with dynamic spatial trajectory analysis and abnormal motion correction, and therefore, the accuracy and the reliability of the method are improved. According to the multi-parameter joint recognition strategy in the dynamic scene, interference caused by environment change and human intervention is reduced, the reliability of target identity judgment in a complex place is improved, and the practicability and the real-time recognition capability for the multi-source scene are enhanced.

The present invention provides a method of masked face recognition from images by artificial intelligence technology including four steps: step 1: generating the images of face wearing a mask; step 2: training a deep learning model for face detection while wearing a mask; step 3: training the deep learning model for face feature extraction while wearing a mask; step 4: building a full pipeline of masked face recognition from images using the trained models from step 2, step 3, and some post-processing algorithms. The method aims to improve the accuracy of identity verification in the context of wearing masks has become popular and compulsory in life.

A system and a method of performing face recognition may include: receiving a first facial image, depicting a first face, and a second facial image depicting a second face; applying an ML model on the first image, to produce a first representation vector, and applying the ML model on the second image to produce a second representation vector; comparing the first representation vector and the second representation vector; and associating the first face with the second face based on the comparison, where the ML model is trained to produce the representation vectors from the facial images, based on regions in the facial images that correspond to distinctiveness scores that are beneath a distinctiveness threshold.

The embodiment of the invention provides a classroom attendance checking method based on multi-source data fusion, and the method comprises the steps: obtaining a face confidence coefficient, a seat occupation confidence coefficient and a clock-in confidence coefficient corresponding to each student identifier in a preset time window, the face confidence is used for representing the matching degree of feature data obtained based on the face image and pre-stored feature data; the seat occupancy confidence is used for representing an overlapping degree between a target space area obtained based on the face image and a pre-stored target seat area; the clock-in confidence is used for representing the effective probability of clock-in; performing weighted summation on the face recognition confidence coefficient, the seat occupation confidence coefficient and the card punching confidence coefficient of the target student identifier to obtain the attendance confidence coefficient of the target student; and the real-time attendance state of the target student is output based on the attendance confidence of the target student, and the real-time attendance state at least comprises class arrival, non-class arrival and abnormal recheck.

The invention relates to the field of deep learning, in particular to a face recognition method based on deep learning in a cloud computing scene. The method comprises the following steps: S1, collecting face image data, and preprocessing the image data through OpenCV; s2, positioning key points of a human face based on an MTCNN large model, and decomposing the human face into different parts; s3, performing feature extraction on the key points; s4, based on a key point detection result, performing face alignment by adopting a variation affine transformation algorithm; s5, performing face comparison according to the features of the integrated parts and the facial features, and calculating a comprehensive score based on a variation similarity coefficient algorithm; s6, generating a public key and a private key based on an RSA algorithm; s7, encrypting the face information by adopting a homomorphic encryption algorithm based on the public key; and after encryption is completed, the data are stored to a large cloud computing model, so that flexible comparison of subsequent repeated data is facilitated. The application of the homomorphic encryption technology ensures the security of the face information in the storage and transmission process.

The invention relates to the technical field of personal privacy protection, in particular to a glasses system for face privacy protection. The system comprises a glasses body and a bearing and pattern presenting component. The physical shell is detachably mounted on the spectacle frame and printed with a cured pattern, the first display module is arranged on the outer surface of the spectacle frame, and the second display module is arranged on the outer side of the lens. Wherein the presented antagonistic interference pattern is generated by a pattern generation module through a joint optimization algorithm, the algorithm takes minimization of the feature similarity of the face recognition model as an antagonistic loss target and takes an approximate preset style image feature as a style loss target, and displayable constraints can be added to ensure the physical realizability of the pattern. According to the invention, by presenting the specific optimized pattern in the key area of the glasses, the face recognition of an external camera can be effectively resisted in a non-interference manner, and the privacy is guaranteed while the beauty and practicability are achieved.

This invention discloses an intelligent terminal system that changes the interface skin according to environmental changes, relating to the field of communication technology. The system includes the following steps: acquiring the driver's gender information and determining the main color scheme of the interface based on the gender information; collecting the driver's facial emotions and generating a face recognition result; collecting the brightness value inside the bus and comparing the brightness value with a set threshold to obtain the current brightness level; acquiring the current location information of the bus and connecting to a server to obtain the current weather conditions based on the location information; comprehensively analyzing the obtained data and searching and configuring it with an interface database, outputting the configured result, and changing the current interface skin to the updated interface skin to complete the skin change; judging the driver's emotional state based on the skin change result and providing reminders to the driver accordingly. This invention not only changes the traditional impression left by in-vehicle terminal devices on customers but also improves vehicle driving safety and enhances user satisfaction.

The invention relates to a multi-task face sensing method and device, electronic equipment, a storage medium and a computer program product. The multi-task face sensing method comprises the steps that a to-be-detected face picture is acquired; a to-be-detected face picture is input into the multi-task face perception model, face features and positioning reference points are extracted from the face picture through the multi-task face perception model, and the positioning reference points are used for executing a positioning task about face key point detection; semantic reference points are calculated based on the positioning reference points through a multi-task face perception model, and the semantic reference points are used for executing semantic tasks about face state detection; and executing a positioning task and a semantic task based on the face features, the positioning reference points and the semantic reference points through a multi-task face perception model. Therefore, by explicitly utilizing the physical representation of the face key points, effective supervision for the semantic reference points is realized, the guiding effect of the face key points on the face perception task is enhanced, and the prediction effect of the face perception model can be improved.

The application discloses a sperm bank identity verification system and method based on face recognition, and belongs to the technical field of information management. The sperm bank identity verification system and method solve the problem that the three-dimensional face recognition device of the existing system cannot complete identity verification under different angles and postures, and delay the system recognition speed and user waiting time. The method comprises the following steps: collecting facial feature information in real time, executing an identity recognition model, and identifying and analyzing the facial feature information by taking the sperm bank associated personnel information as prior information. The identity recognition model is iteratively trained by a training set and a test set, the facial feature information is identified and analyzed by taking the sperm bank associated personnel information as prior information, the feature attention of the facial feature information under multiple visual angles and postures is analyzed by taking the sperm bank associated personnel information as prior information without affecting the verification accuracy, the sperm bank identity verification accuracy is improved, and the model calculation amount is reduced.

The embodiment of the invention provides a deep false detection method and device based on feature decoupling and double-branch enhancement, and the method comprises the steps: extracting a real identity feature of a to-be-detected face image through a pre-training face recognition model of freezing parameters, inputting a to-be-detected face image into a trainable deep learning network, extracting an identity correlation component and a counterfeit correlation component at the same time, constraining the identity correlation component to approach a real identity feature, and constraining the counterfeit correlation component to be orthogonal or low-correlated with the identity correlation component in a feature space at the same time; and inputting the constrained counterfeited related component into a double-branch counterfeited enhancement module for feature enhancement, and performing counterfeited detection through the enhanced counterfeited enhancement feature to obtain a detection result, thereby improving the precision of deep-false detection.