570 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 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 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 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 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 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 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 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.

The invention discloses a face forgery detection method and system based on a space-time hyperbolic hypergraph in the technical field of image processing, and aims to solve the problem that the internal feature relation of a face structure is difficult to capture in the prior art. The method comprises the following steps: inputting a face video of which the authenticity is to be detected into a trained face counterfeiting detection model; performing feature extraction on the face video of which the authenticity is to be detected through a face feature partition extraction module to obtain face local features; according to the face local features, constructing a space-time hypergraph through a space-time node hypergraph construction module; performing semantic classification feature extraction on the space-time hypergraph through a hyperbolic hypergraph learning module to obtain classification features; and inputting the classification features into a classifier to obtain a true or false detection result. According to the invention, high-accuracy and high-generalization face counterfeiting detection can be realized, the method can be widely applied to the fields of security and protection, finance and the like, and reliable guarantee is provided for related face recognition application.

The invention discloses a face anomaly detection method and device, equipment and a storage medium, and belongs to the technical field of face recognition. The method comprises the steps of firstly obtaining to-be-detected face data; performing living body detection based on the to-be-detected face data to obtain a living body detection result; and if the living body detection result is qualified, performing face recognition detection based on the to-be-detected face data, and executing corresponding processing operation according to the obtained face recognition detection result. According to the invention, the living body detection and the face recognition detection are successively carried out based on the obtained to-be-detected face data, so that the face recognition detection result is finally obtained and the corresponding processing operation is executed, and effective detection of the abnormal face condition is realized.

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.

Face recognition systems are vulnerable to the presentation of spoofed faces, which may be presented to face recognition systems, for example, by an unauthorized user seeking to gain access to a protected resource. A face liveness detection method that addresses this vulnerability utilizes motion analysis to compare the relative movement among three regions of interest in a facial image and based upon that comparison to make a face liveness determination.

The invention relates to the technical field of face recognition, and provides a face recognition model training method and device based on face shielding, equipment and a medium, and the method comprises the steps: carrying out the reasoning of a face training set through a pre-trained face recognition model, and obtaining a reasoning result; dividing the face training set into a face simple set and a face difficult set according to the reasoning result; increasing the proportion of the face difficulty set in the face training set to obtain a new face training set; and training the pre-trained face recognition model by using the new face training set to obtain a target face recognition model. Through the technical scheme, the recognition accuracy of the model on the difficult face is improved, and the model precision is improved, so that the discrimination of attack means such as face shielding attack is realized.

The invention discloses a multi-camera-position-based face information acquisition method and device, equipment and a medium, and relates to the field of face recognition, and the method comprises the steps: collecting a fixed-focus picture image through a fixed-focus camera, and recognizing a head region and a face region in the fixed-focus picture image; determining the overlapping degree of the head area and the face area, and judging whether the overlapping degree meets a first preset condition or not; if the overlapping degree meets a first preset condition, determining head posture information in the head region, so as to determine a current face orientation based on the head posture information, and judging whether the current face orientation meets a second preset condition or not; and if the current face orientation meets a second preset condition, executing a zoom operation through the zoom camera so as to amplify a face picture in the head area and collect a corresponding first zoom picture image, and obtaining corresponding face information from the first zoom picture image.

The invention discloses a face recognition application method and system combined with intelligent glasses. The method comprises the following steps: collecting multi-dimensional data based on intelligent glasses, the multi-dimensional data comprising a facial expression sequence, an emotion curve and a voice keyword; judging an acquisition frequency difference of the data of each dimension through a preset threshold value to obtain a frequency adjustment coefficient; performing sampling rate standardization processing on the multi-dimensional data based on the adjustment coefficient; and marking a timestamp label and a scene label on the standardized multi-dimensional data, carrying out scattered storage on data fragments through an index mechanism of a distributed database, and obtaining a unique identifier after storage and the like. According to the method, multi-dimensional data such as facial expressions, emotions and voices are collected through the intelligent glasses, and intelligent adjustment and standardization processing are performed on the sampling frequency. And efficient storage and retrieval of mass data are realized by utilizing a distributed database and a time axis alignment algorithm.

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 discloses a face recognition method based on adaptive sine angle robust principal component analysis, and belongs to the technical field of face recognition, and the method comprises the steps: obtaining a face image, and carrying out the preprocessing of the face image; constructing a robust principal component analysis model based on an adaptive sine angle, and training the robust principal component analysis model by using the training set; solving an optimal projection matrix by using a non-greedy iterative algorithm; mapping the original face features to a discrimination space by using the optimal projection matrix; and performing face recognition by using a nearest neighbor classifier, and performing robustness evaluation on a recognition result. According to the method, the adaptive sine angle is added, abnormal values can be effectively suppressed in the face of noise data, the influence of the abnormal values on the projection direction is reduced, and the method plays a key role in improving robustness.

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.