70 results about "Face identity" patented technology

The invention provides a face image restoration method based on generative adversarial network inverse mapping, and belongs to the technical field of image restoration and computer vision. According to the method, an encoder-decoder architecture is adopted, an encoder is a ResNet combined with a channel attention mechanism and can encode a to-be-restored face image (containing block-shaped shielding) to a potential space of a StyleGAN, and a W + vector of 18 * 512 dimensions is obtained; and the decoder is a pre-trained StyleGAN generator, and can convert the potential vector into a complete face image to realize restoration. In the training process, through a weighted combination constraint model of L2 loss, perception loss and face identity loss, it is ensured that a restoration result is consistent with an original image in pixel, feature and identity levels. Meanwhile, by means of the decoupling characteristic of the StyleGAN potential space, the repaired face features (such as smile and age) can be edited. According to the method, complex preprocessing is not needed, large-area missing face images can be efficiently repaired, identity consistency is kept, and the method is suitable for monitoring image enhancement, old photo repair and other scenes.

The invention relates to the technical field of face verification, and discloses a face identity verification data processing method based on dynamic feature extraction, which comprises the following steps: acquiring a continuous face video frame sequence and calculating a full-pixel instantaneous velocity vector to generate an original dense optical flow field, selecting rigid region anchor points to calculate a rigid affine transformation matrix and construct a theoretical rigid motion field, performing differential stripping on the theoretical rigid motion field from the original dense optical flow field, and extracting a non-rigid micro-motion residual field; mapping the non-rigid micro-motion residual field to a facial muscle topological grid to generate a time sequence feature tensor, and calculating a geodesic line distance between a covariance matrix of the time sequence feature tensor and a reference dynamic feature in a Riemannian manifold space; when the geodesic distance is smaller than a threshold value, verification is passed, through a rigid-non-rigid orthogonal decomposition mechanism, the special viscoelastic micro-motion and cooperation law of biological soft tissue is captured by utilizing a residual field, and the high-simulation mask is effectively defended.

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

The invention provides a personalized face image restoration method, device and equipment, a medium and a program product, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring a low-quality image and a reference image; inputting the low-quality image and the reference image into a face image restoration model for restoration processing, and outputting a personalized face restoration image; the face image restoration model is obtained by performing personalized training on a pre-trained diffusion model based on a low-quality image sample and a reference image sample; the face restoration image is represented as a personalized image with the same face structure as the low-quality image and the same face identity as the reference image. According to the embodiment of the invention, the defect of insufficient restoration precision in the prior art is overcome, general structure fidelity and identity detail control are considered through the trained face image restoration model design, and the method is suitable for the restoration task of the seriously degraded image and has high generalization ability.

The present disclosure belongs to the technical field of image processing. Provided are a video generation method and apparatus, and an electronic device and a computer program product. The video generation method comprises: extracting a facial identity feature of a target virtual human by using a facial recognition model; extracting an audio feature vector and an emotion prompt word from target audio by using an audio classifier; and using the audio feature vector and the emotion prompt word as control features, merging the control features with a diffusion model, using the facial identity feature to control the output of the diffusion model, and using the diffusion model to generate speech video data of the target virtual human, which speech video data matches the target audio. The technical solution of the present disclosure can generate a speech video stream of a virtual human with natural facial expressions.

The invention provides a classroom attendance checking method based on face recognition, computer equipment and a storage medium, and the method comprises the steps: carrying out the face feature extraction of a classroom image in a classroom time period, and obtaining a face feature set, the classroom time period being a time period from the beginning of classroom teaching to the end of classroom teaching; matching the face features in the face feature set with preset face features in a face identity database to obtain a first result set; the face identity database comprises a plurality of identities and preset face features corresponding to the plurality of identities; according to a first face feature in the face feature set, updating a preset face feature in a face identity database; matching a second face feature in the face feature set with a preset face feature in the updated face identity database to obtain a second result set; and combining the first result set and the second result set to generate an attendance result corresponding to the classroom period. According to the technical scheme, the attendance success probability can be improved.

The present application relates to the technical field of computer vision security, and discloses a security management method based on face recognition, which is used to solve the technical problem that it is difficult to stabilize the association of the same person's face identity in the multi-camera cross-point and cross-time monitoring of the traditional method, which is affected by light, posture, shielding and fluctuation; the method first constructs the passing relationship of the camera point and unifies the time reference, collects the snapshot image and generates the collection condition label, executes idempotent deduplication and sequential rearrangement on the warehouse data; based on the state dictionary and hierarchical mapping table, the snapshot record is quality graded, and multiple observation sequences are organized according to the observation window rule; under the constraint of the compatible rules of the association time window and the collection condition, the candidate association pair is generated and the evidence package is converged, and after the hierarchical confirmation and conflict shunting, it is written into the versioned identity relationship graph, the incremental association is executed according to the latest effective version, and the invalidation rule is set for the records to be reviewed, which realizes the continuous and traceable security management of the cross-point identity chain.

The subject technology receives an input image and a segmentation mask of the input image. The subject technology obtains reconstructed noise of the input image using the input image and the segmentation mask. The subject technology determines a first set of features by performing a first portion of a forward pass of the reconstructed noise through a decoder. The subject technology determines a second set of features by processing the input image for stable diffusion using an image to image (IMG2IMG) model. The subject technology generates a third set of features based on combining, using the segmentation mask, the first set of features and the second set of features with the reconstructed noise. The subject technology generates an output image by performing a remaining portion of the forward pass of the third set of features through the decoder.

The invention is suitable for the technical field of computer vision and three-dimensional data processing, and provides a face recognition detection method and device, computer equipment and a program product, and the method comprises the steps: obtaining the shielding measurement corresponding to each first data point in a three-dimensional face point cloud; on the basis of the shielding measurement corresponding to each first data point, performing shielding self-adaptive graph convolution operation on the three-dimensional face point cloud to generate an initial feature graph; wherein the initial feature map comprises shielding points and non-shielding points; for the shielding points in the initial feature map, combining a preset face geometric prior model and a feature manifold formed by non-shielding points, performing feature completion on the shielding points, and generating a complete feature map; and based on the complete feature map, performing face recognition detection, and determining a face identity, thereby improving the face recognition accuracy in the shielding scene.

The present application relates to the technical field of face image editing, in particular to a kind of face image editing method based on multi-modal interactive module of scribble. Through two-way multi-modal interactive module, cross attention graph is calculated from position dimension and channel dimension respectively, the cross attention graph obtained and scribble vector are used to carry out latent space latent reflection on face image latent vector iterative modification, complete scribble and image target editing position semantic content alignment, and scribble is embedded into corresponding latent space;Through single-path multi-modal interactive module, save the original face identity feature after editing, the texture supplement vector obtained carries out latent space latent reflection on face image latent vector iterative modification, and finally generates face image editing result.The present application can more intuitively and fully express the editing intention of user, realize the real feeling editing effect that meets user's expectation, and the present application has superiority in editing effect, editing real feeling and face identity information saving of face image editing.

The application discloses a face recognition method and device based on a security system and a storage medium, when a person approaches a security area, the security system automatically starts a face recognition function, collects face information data, uploads the collected face information data to the cloud after preprocessing, performs face recognition based on an intelligent face database, and outputs a recognition result; when the recognition result is 1, the face identity authentication is passed, and the person can enter the security area; when the recognition result is 0, the recognition result is displayed on a terminal and a corresponding warning is sent. The face recognition method of the security system has the characteristics of artificial intelligence, that is, no additional personnel is arranged for operation, the security system automatically judges and handles the conditions according to the set recognition result; the face recognition method of the security system is accurate and reliable by using corresponding algorithms, and can correctly distinguish between the personnel who can enter the security area and strangers.

1. The name of the design product: terminal for receiving cases. 2. The use of the design product: the design product is used in the police station to receive the hall, the intelligent device integrating the functions of face identity collection and identification, self-service registration of alarm information, self-service uploading of alarm evidence, and self-service case progress inquiry. 3. The design points of the design product: in shape. 4. The picture or photo best indicating the design points: perspective view 1. 5. The unusual surface, omit the top view.

The invention relates to the technical field of animal husbandry management, and discloses a sheep face identity authentication method for full life cycle tracing of agricultural and pastoral individuals, and the method comprises the following steps: building an exclusive biological evolution rule model for describing the evolution of the face structure of each registered sheep individual along with time based on the historical face image sequence of each registered sheep individual; obtaining a current face image of a to-be-authenticated sheep individual and extracting a to-be-authenticated face structure state vector; predicting a theoretical face structure state vector of each registered sheep individual at the current time point by using the model; and calculating a residual vector between the to-be-authenticated vector and each theoretical vector, and determining the individual identity of the to-be-authenticated sheep based on the statistical measure (such as mahalanobis distance) of the residual vector. Static feature comparison is replaced by the dynamic model, continuous and reliable authentication of the whole life cycle of the individual is achieved, health monitoring and breeding auxiliary application are fused, and the comprehensive efficiency of fine management of the agricultural and pastoral individuals is effectively improved.

This invention provides a facial recognition method, apparatus, and electronic device. The method involves acquiring a target image and the age parameter of a face to be recognized within the target image; extracting target facial features from the target image; and determining whether the face to be recognized matches the target identity based on the age parameter, the target facial features, and the feature classification results corresponding to the target identity. The feature classification results include standard facial features for multiple age groups corresponding to the target identity. This approach takes into account that a person's appearance changes with age. Therefore, during facial recognition, the facial features of the face to be recognized are matched with standard facial features that match its age parameter, thereby improving the accuracy of facial recognition.

The application belongs to the computer field and discloses a face identity recognition method, device, equipment and storage medium. The method comprises the following steps: projecting a face image to be verified into the same plane as a standard face image to obtain a target face image; determining a scaling ratio of the target face image and the standard face image; determining an Euclidean distance between the face image to be verified and the standard face image according to the scaling ratio; and determining a face identity recognition result according to the Euclidean distance. The above method can correct multi-pose faces without camera calibration and measurement of a snapshot face deflection angle, reduce the sensitivity of personnel identity comparison to the face deflection angle, and improve the face identity recognition efficiency.

The invention provides a multi-scene video conference face identity verification method and system based on deep learning, and relates to the technical field of face recognition, and the method comprises the steps: collecting a real-time video stream of a video conference participant, and extracting a standardized face image; extracting global scene information to generate a scene description vector, and calculating the similarity with a preset scene template to obtain an adaptive weight; the feature map is decomposed according to a pyramid structure, the spatial correlation degree between hierarchies is calculated and self-adaptive fusion is carried out, and the similarity between the fusion feature vector and the identity feature template is calculated to determine the verification result, so that the method can adapt to various illumination and attitude change scenes, and the face identity verification accuracy is improved.

This invention proposes a method and system for generating adversarial attack samples for faces based on attribute editing. It maps the latent space of the input real face image and the target image to be identified into a low-dimensional manifold representation. Simultaneously, it improves the similarity of facial identity information through feature layer fusion. The attribute editing step optimizes the editing intensity of the optimal edited attributes obtained in the attribute selection step by designing a set of attribute editing parameters. It also introduces various controls to optimize image generation effects, achieving the best model attack success rate while maintaining the edited image quality and the visually recognizable identity information. This is used to test the robustness of the face recognition model and fine-tune the model to improve its recognition accuracy.

The invention relates to a face feature identity verification method based on an RK3588 platform, and belongs to the field of biological feature identity verification. The method comprises the steps of algorithm model design, model selection and strategy optimization, model optimization and performance verification, NPU resource fine configuration and parallel processing strategy and system integration and application function implementation. According to the face feature identity verification method based on the RK3588 platform, the RetinaFace convolutional neural network model is optimally designed, the MobileFaceNet convolutional neural network model is optimally designed, effective model quantization and conversion are achieved, a refined three-core NPU resource parallel scheduling strategy is achieved, and system integration is improved. On the premise of ensuring relatively high recognition accuracy, the real-time processing capability of face identity verification on the edge equipment is remarkably improved.

The invention discloses a student online learning video analysis type low-consumption identity recognition method and system, and relates to the technical field of data analysis. The method comprises the following steps: constructing a to-be-detected video data set; analyzing the to-be-detected video data set by using a video frame optimization model based on a time sequence behavior mode, and selecting an optimal video frame; determining a face region by using a self-adaptive face detection model based on regional spectrum migration and skin color constancy in combination with the optimized video frame; and performing recognition analysis on the face region by using the three-dimensional face identity recognition model based on hierarchical sparse coding to generate an identity recognition result. The method can effectively improve the recognition precision and reduce the consumption of computing resources.

The invention relates to the technical field of computer software and service routing scheduling, and discloses a face verification multi-service provider intelligent routing method, system, device and medium, the method comprises the following steps: receiving a face verification request, the face verification request comprising user identity information to be verified; determining at least two candidate service providers which can be used for providing face verification service; calculating a routing decision score of each candidate service provider according to the real-time performance index corresponding to each candidate service provider in combination with a preset routing strategy; based on the routing decision score of each candidate service provider, determining the candidate service provider with the highest routing decision score as a target service provider; and calling a face verification interface of the target service provider to complete verification of the user identity information, and returning a verification result. According to the invention, the problems of limited cost control, low resource utilization efficiency, lack of redundancy of service availability and service interruption caused by a single service provider architecture are solved, and the insurance service continuity and the operation reliability are improved.

The application discloses a face image identity privacy protection method and device, the method comprises the following steps: constructing a feature learning model, extracting features of an acquired face image by the feature learning model, and decoupling statistical information and background information in the face image by a face identity feature decoupling subnetwork model; integrating the features extracted by the feature learning model and the statistical information to obtain integrated information; constructing an identity disturbance model, adding random disturbance to the integrated information based on a privacy level of a face image user; and constructing an image reconstruction model, reconstructing the face image based on the integrated information after adding random disturbance. The method of the application can effectively resist background knowledge attacks, reconstruction attacks and combination attacks, realize provable and controllable face identity privacy protection, and ensure the optimization trade-off between the privacy and usability of the published image.

To provide authentication technology that enables enhanced authentication accuracy while allowing users to enjoy the benefits of facial recognition-enabled gate devices.SOLUTION: An authentication device performs a predetermined subtraction operation on an authentication threshold to be compared with a score corresponding to the collation result between a face registration image linked to registration information and face image information of an entering person, allowing the collation result between the face registration image linked to the registration information and the face image information of the entering person to be prioritized over the collation result between the face registration image not linked to the registration information and the face image information of the entering person. The authentication device authenticates whether the person corresponding to the face registration image used to obtain the collation result with the highest difference between the score and the authentication threshold, among collation results favored because the face registration image is linked to the registration information and collation results not favored because the face registration image is not linked to the registration information, is the same person as the entering person.SELECTED DRAWING: Figure 1

The application is suitable for the technical field of computer vision and three-dimensional data processing, and provides a face recognition detection method, device, computer equipment and program product. The method comprises the following steps: obtaining a shielding metric corresponding to each first data point in a three-dimensional face point cloud; performing a shielding adaptive graph convolution operation on the three-dimensional face point cloud based on the shielding metric corresponding to each first data point, to generate an initial feature map; wherein the initial feature map comprises shielding points and non-shielding points; for the shielding points in the initial feature map, combining a preset face geometry prior model and a feature manifold composed of non-shielding points, performing feature completion on the shielding points, to generate a complete feature map; performing face recognition detection based on the complete feature map, to determine a face identity, thereby improving the face recognition accuracy in a shielding scene.

The invention discloses a campus spoofing behavior recognition method and system based on multi-mode and edge intelligent fusion, and relates to the technical field of artificial intelligence, and the method comprises the steps: receiving audio data and video data, carrying out the data enhancement of the audio data and the video data, obtaining the enhanced audio data and video data, and carrying out the recognition of the enhanced audio data and video data; generating an audio and video data set based on the enhanced audio data and video data; the audio and video data set is input into a pre-established AVL multi-mode behavior analysis model, behavior analysis results are output and obtained, and the behavior analysis results are divided into abnormal results and non-abnormal results; when the behavior analysis result is a non-abnormal result, identifying that the behavior is a non-spoofing behavior; when the behavior analysis result is an abnormal result, it is recognized that a spoofing behavior exists, a face image is obtained, face identity recognition of a spoofing person is conducted on the basis of the face image, and the spoofing person is confirmed on the basis of the face identity recognition result of the spoofing person.

The invention relates to the field of biological feature recognition and information security, in particular to a face revocable template protection method based on deep hash. The method comprises the following steps: detecting and aligning an input face image, and extracting and normalizing features; performing projection and symbol quantization to obtain a binary vector with a fixed length; deriving a dimension permutation and index subset by using a user key, and generating a protected template; registration and identification are completed in a protected domain by a Hamming distance; when the template is leaked or needs to be replaced, a new template is regenerated by updating a secret key or an index rule, and template revocation and non-linkability are achieved. The system side only stores the protected template and necessary verification parameters and does not store plaintext images and unprotected features, so that the method is low in calculation overhead, convenient to deploy and suitable for face identity authentication of end-side equipment and a cloud platform.

The invention provides a face changing model training method, an image face changing processing method and electronic equipment, and the method comprises the steps: obtaining a first sample image set, training a first image processing model through the first sample image set, and keeping non-face identity features when the face changing operation is executed; obtaining a second sample image set, and training the first image processing model through the second sample image set to recognize human face identity features; acquiring a third sample image set, and when the first image processing model is trained through the third sample image set to execute a face changing action, retaining non-face identity features and replacing the face identity features; and obtaining the trained first image processing model as a target face changing model. In this way, the decoupling processing effect of the model for the face identity features and the non-face identity features is improved, and therefore the replacement effect of the face features in the model output result is improved.

The invention provides a voice-driven video synthesis method and device and a storage medium, and relates to the technical field of computer vision and artificial intelligence. The method comprises the following steps: respectively determining multiple frames of face region images and corresponding face identity feature images according to multiple frames of original images in an original speaking video; a face standardization model is adopted, according to each frame of face identity feature image, identity feature and mouth shape motion decoupling processing is carried out on the corresponding face region image, and a corresponding mouth closing face region image is output; generating a plurality of frames of speaking face region images according to the plurality of frames of mouth closing face region images and the audio data by adopting a mouth shape synthesis model; and carrying out fusion processing on the multiple frames of speaking face region images and the multiple frames of original images to obtain a target speaking video matched with the audio data. The interference of the original mouth shape is eliminated, the synchronization quality of the speaking mouth shape and the audio data is higher, and then the synchronization of the mouth shape and the audio data in the target speaking video is remarkably improved.

The invention belongs to an image processing technology, and particularly relates to a face identity privacy protection method and system based on makeup style migration. The method comprises the following steps: training a conditional diffusion model by using a face image and corresponding identity features thereof, so that the conditional diffusion model has the capability of generating an image with specific identity features; inputting the identity features of the original image and the target image into a pre-trained diffusion model, and guiding a sampling process through multi-modal information (such as identity semantics, structural constraint, makeup migration and the like) to generate a new image which is highly similar to the original image visually but cannot be recognized by a machine; according to the invention, it is ensured that the processed image is visually similar to the original image, and it is ensured that the processed image effectively destroys identity information which can be identified by a machine, so that image functionality and privacy security are both considered in a privacy protection scene.

The invention relates to the technical field of artificial intelligence, and provides a thermal infrared face image processing method, and the method comprises the steps: receiving a low-resolution thermal infrared face image, and carrying out the contrast enhancement and gray normalization processing of the thermal infrared face image, and obtaining a preprocessed image; converting the preprocessed image into a visible light face image through an axial attention generator network; in the training process of the axial attention generator network, performing block-level discrimination on the generated visible light face image and the corresponding real visible light image by using a block discriminator network, and generating a discrimination probability matrix to calculate an adversarial loss function LG; utilizing a pre-training deep network to extract and generate a face identity feature vector of the visible light face image; and performing similarity matching on the face identity feature vector and a pre-stored feature in a database, and outputting a recognition result. According to the technical scheme of the invention, through end-to-end cross-modal conversion and native low-resolution processing, the accuracy and efficiency of thermal infrared face recognition are remarkably improved.

The present application relates to the field of artificial intelligence and computer vision technology, and particularly relates to a face identity exchange method, system, device and storage medium, and specifically as follows: inputting a source face image and a target face image into a preset face identity exchange model to perform face identity exchange, the model comprising a latent space encoder, an identity feature extractor, a Transformer mapping network, a latent space identity injection module, a distillation diffusion refining module and a reconstruction decoder, after feature extraction, knowledge distillation learning and decoding, an exchanged face image is obtained, face identity exchange is realized, and the model is optimized and trained through calculation of a loss function. The present application can present better effects in identity preservation, expression restoration and visual fidelity during face identity exchange by introducing a distillation diffusion and attention fusion mechanism.