106 results about "Face identity" patented technology

The invention provides a method for editing facial image attributes based on a text of a diffusion model. The method realizes flexible facial editing with high quality and identity consistency. The method comprises the following steps: constructing a description control face diffusion model comprising a noise prediction network and a variational auto-encoder, wherein the noise prediction network comprises a plurality of text alignment face Transform modules and an optimization residual feature module; the method comprises the following steps: inputting an original image and target text description based on a pre-training model, optimizing a target embedding generated by the input target text description by a text alignment face Transform module to obtain an optimized embedding, and performing model fine tuning after optimization is completed; and performing linear interpolation on the target embedding and the optimized embedding to obtain an initial editing result, introducing ArcFace-Loss as identity loss, extracting face features of an original image and the initial editing result through a pre-training model, calculating feature similarity and minimizing the loss, and ensuring consistency of face identities after editing.

The invention discloses an automatic conference summary generation method based on multi-source heterogeneous information fusion. The method comprises the following steps: extracting facial features of a speaker by using a face recognition technology and recognizing a face identity of the speaker; acquiring an audio signal through a microphone array, and recognizing a voiceprint identity of a speaker by using a voiceprint recognition technology; in combination with video and audio information, through a multi-source heterogeneous information fusion technology, alignment of video and audio data is carried out in time; through a sound source positioning technology, a voiceprint identity and a face identity are matched and aligned in space, and the identity of a speaker is accurately positioned and recognized; the spokesman and the speaking content are calibrated and separated, and it is ensured that the identity of the spokesman is accurately matched with the speaking content; and generating a spokesman abstract and a conference summary according to the calibrated speech content by using natural language processing and a deep learning model. The method is suitable for various conference scenes, the speaking content of different spokesmen can be recognized, the spokesman abstract is generated, and the efficiency and accuracy of conference summary generation are improved.

According to the single-image geometric texture joint control image generation method and the terminal, an original image and text prompt information transmitted by a user are received, and a human face sub-image, a key point image and a front face image are generated based on the original image; performing feature extraction and processing on the human face sub-images to generate human face identity implicit vectors; generating a hidden space control vector based on the key point graph and the frontal face image; generating a text embedding feature based on the text prompt information; injecting the human face identity implicit vector, the text embedding feature and the implicit space control vector after splicing the noise vector into a diffusion converter model by adopting a hierarchical selective feature injection mode, and generating an identity holding image by combining a decoder; the diffusion converter model is obtained through training by adopting a low-rank adaptive training strategy; and controllable generation of the identity maintaining image with single image driving and multi-pose styles is realized.

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 relates to the technical field of computer vision, and provides a figure image generation method and device, electronic equipment and a readable storage medium. The method comprises the following steps: acquiring text features corresponding to figure description information, and extracting face identity features, face key point features and face reference image features; inputting the text feature and the first image feature into a preset diffusion model to obtain a text processing feature and a first image processing feature; obtaining a second image feature, and inputting the second image feature into a preset diffusion model to obtain a second image processing feature; based on the face identity feature, the face key point feature and the face reference image feature, obtaining a face identity comprehensive feature, and inputting the face identity comprehensive feature into a preset diffusion model to obtain a third image processing feature; and obtaining a target image feature vector based on the text processing feature and the third image processing feature, and generating a target image. Through the technical scheme provided by the invention, the quality and accuracy of image generation are improved.

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 belongs to the technical field of image processing, and discloses a text-driven face editing method based on multi-modal fusion, and the method comprises the steps: taking a to-be-processed source image and a text prompt as input; mapping the initial implicit code to a vector space through a mapping network to obtain an intermediate implicit code; respectively inputting the intermediate implicit code and the implicit code corresponding to the source image into a generator to obtain a first generated image and a second generated image; and constructing total loss by using the text loss, the style loss and the face loss, optimizing the generated image hidden code by using the total loss, and generating a final face image. According to the method, a StyleGAN semantic network is improved, text and image features are aligned through a CLIP pre-training model, and face image features before and after editing are aligned by using a face recognition network, so that a face editing image with high quality and good effect is generated, attribute decoupling is realized, and consistency of face identities is kept.

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 invention relates to a face identity exchange method based on multi-scale feature transfer, and belongs to the technical field of image-level deep counterfeiting. The method comprises the following steps: acquiring a source face image and a target face image; inputting the two images into a unified multi-scale encoder to obtain multi-scale source face features and multi-scale target face features; the multi-scale source face features pass through an identity mapping network to obtain a multi-scale source face identity vector; a first source face identity vector in the multi-scale source face identity vectors and a first target face feature in the multi-scale target face features are fused through a cross attention fusion module to obtain a preliminary fusion feature; and inputting the multi-scale source face identity vector and the multi-scale target face features into a multi-scale feature transfer decoder to obtain an exchange face image. According to the method, the accuracy of identity information detail transfer can be improved, and a high-fidelity exchange face with a source identity and a target attribute is generated.

The invention relates to the technical field of computer vision, in particular to a depth forgery detection method and system for priori knowledge guidance and double-domain representation optimization, and the method specifically comprises the following steps: obtaining a to-be-detected video containing a face identity; preprocessing the video data to be detected to obtain a face image; inputting the face image into a pre-trained deep counterfeiting detection model, and extracting deep counterfeiting evidence features of the image; the deep forgery detection model guides the feature extractor to learn potential knowledge for a real image in the pre-training model through priori knowledge, and establishes a perception ability for the real world; the robustness and generalization of the detection model are improved through an adaptive data augmentation mechanism of a feature hidden space and an image space; and the features are used as counterfeiting evidences for judging whether the facial image is deeply counterfeited or not. The method is lower in test cost, higher in efficiency and better in generalization, and can widely cope with deep counterfeit detection scenes.

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 discloses a single-sample cross-domain movie and television play character identity recognition method and system. The method comprises two key parts of single-sample cross-domain face recognition based on link contrast learning (LCL) and progressive identity propagation based on appearance similarity. According to the invention, a face recognition task with a single portrait reference is processed through an LCL method. According to the method, the internal structure of a target domain is mined by using prototype comparative learning, feature discrimination is enhanced through intra-domain link comparative learning, and semantic alignment of a source domain and the target domain is realized by means of a bidirectional matching mechanism and inter-domain link comparative learning, so that the domain difference and sample sparsity are overcome, and meanwhile, the identity of a person containing a human face in a video is accurately recognized. And then constructing a figure identity-based association graph, and propagating the identified face identity information to a figure sequence, which does not contain a face, in the video through appearance similarity clues by adopting a progressive multi-strategy label propagation mechanism, so as to finally realize accurate identification of wider figure identities in the video.

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 invention relates to a face verification system based on a lensless camera. The system comprises a lensless mask, a camera sensor, and a controller. The lensless mask is used for lensless imaging of a face, and its parameters are optimizable. The camera sensor is used to capture a face image formed after passing through the lensless mask. The controller is used to directly perform face verification based on the face image captured by the camera sensor. The training process of the controller includes: establishing an imaging model for the lensless mask; inputting the face image into the imaging model to obtain a lensless imaging result; then inputting the result into a face verification model to be trained to obtain a face identity prediction result; calculating a loss function based on the face identity prediction result, thereby jointly optimizing and training the imaging model and the face verification model; and the face verification system optimizes the parameters of the lensless mask based on the trained imaging model. Compared with the existing technology, the present invention enhances the security of face data and improves prediction accuracy.

A method and system for use of obfuscation coding. An example method includes a computing system receiving an image that depicts a human face. The method then includes the computing system generating a modified image based on the received image, with the generating of the modified image involving inserting into the received image an obfuscation marker that (i) obfuscates the human face in a manner that prevents a person observing the modified image from identifying the human face and (ii) includes predefined symbology that is interpretable by a machine to identify the obfuscated human face. Further, the method includes using the predefined symbology in the modified image as a basis to take action based on an identity of the human face.

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.

A method and system for use of obfuscation coding. An example method includes a computing system receiving an image that depicts a human face. The method then includes the computing system generating a modified image based on the received image, with the generating of the modified image involving inserting into the received image an obfuscation marker that (i) obfuscates the human face in a manner that prevents a person observing the modified image from identifying the human face and (ii) includes predefined symbology that is interpretable by a machine to identify the obfuscated human face. Further, the method includes using the predefined symbology in the modified image as a basis to take action based on an identity of the human face.

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.

The invention relates to an image generation method and device, a readable storage medium and a program product. The method comprises the following steps: acquiring a reference face image and an original figure image; the reference face image comprises a reference face area, and the original figure image comprises an original face area; extracting identity features of the reference face region based on the reference face image, and extracting a plurality of image features of different feature levels based on the reference face image; fusing the plurality of image features with the identity feature to obtain a plurality of fused features corresponding to the plurality of image features; performing image generation processing based on the plurality of fusion features and the original figure image, so as to edit the original face region according to the reference face region, and generating a target figure image including a target face region obtained by editing; wherein the number of the fusion features is at least two. By adopting the method, the consistency of human face identities in the generated image can be improved.