16 results about "Face analysis" patented technology

The invention discloses an image processing method, electronic equipment and a computer readable storage medium, and belongs to the technical field of image processing. The method comprises the steps that a first face analysis graph of a to-be-enhanced image and a second face analysis graph of a reference image are acquired, the definition of the reference image meets a preset image definition condition, and the to-be-enhanced image and the reference image both comprise a first shooting object; determining a first affine transformation matrix for converting the second face analysis graph into the first face analysis graph; acquiring a first contour feature of the reference image; performing affine transformation on the first contour feature according to the first affine transformation matrix to obtain a second contour feature; and obtaining an enhanced image according to the second contour feature, the to-be-enhanced image and the face enhancement model, the definition of the face region in the enhanced image being greater than the definition of the to-be-enhanced image. The second contour feature carries the feature of the image with high definition in the reference image, so that the trueness and definition of the enhanced image can be improved, and the quality of the image is improved.

The present application relates to the technical field of advanced geological prediction, in particular to a kind of advanced geological prediction system based on information fusion of multiple detection methods, comprising comprehensive analysis subsystem, advanced prediction module and notification and statistics module.The comprehensive analysis subsystem includes face analysis module, advanced drilling analysis module and geological radar analysis module.The present application combines stress-strain monitoring, geological radar detection, advanced drilling and other detection methods, improves the adaptability to complex construction environment, improves the intelligent level, realizes the rapid transmission and sharing of geological information;Through logical judgment program, data centralized analysis is carried out, geological abnormal conditions such as rock burst, discontinuous geological structure are found in time during drilling process, so as to output corresponding early warning signal, which provides more definite decision basis for construction personnel.

Embodiments of the present disclosure provide a face image repairing method and device, electronic equipment and storage medium. The method comprises: obtaining a face image to be repaired; cropping a first face image from the face image to be repaired by a face detection algorithm; analyzing the first face image by a face analysis model to generate a binary face mask image; performing face repairing processing on the first face image by a face repairing model to obtain a second face image; synthesizing a third face image according to the first face image, the second face image and the binary face mask image; and replacing the first face image in the face image to be repaired with the third face image to form a repaired face image. The method analyzes and performs face repairing processing on the face image by the face analysis model and the face repairing model respectively to obtain the binary face mask image and the second face image. Different regions of the face image are selectively repaired according to the binary face mask image, so that the repaired face image is more natural.

The invention discloses a face analysis method based on multi-task learning and transformer, and the method comprises the following steps: dividing an input image into a plurality of non-overlapping windows, and carrying out the feature transformation through a multi-layer perceptron; tasks are classified according to output types, each prediction head has an independent and unique feature fusion module and a decoding module and can learn a plurality of sub-tasks at the same time, finally, corresponding losses are calculated, and a gradient normalization strategy is adopted to ensure that a model does not tend to each task. The invention provides a face analysis architecture based on multi-task learning and transformer, a plurality of related or unrelated tasks are allowed to be processed in a unified model at the same time, and feature representation is shared among different tasks, so that redundancy of feature extraction is reduced, and the resource utilization efficiency is improved. By dynamically calculating the learning weight for different tasks, the multi-task learning framework can synchronize the learning speed of each sub-task to the greatest extent, and an effective convergence effect is achieved.

This invention provides an intelligent interactive media display method and system. The method includes: offline construction of a visitor image library; capturing faces of people standing in front of the media wall using a camera installed on the media wall, and extracting qualified facial images using face detection technology; then, using face analysis technology to perform attribute analysis on the extracted qualified facial images to determine the gender, age, and identity attributes of the faces in front of the media wall; combining the constructed visitor image library with the results of face detection and analysis to intelligently display the media wall; and using gesture recognition technology to allow users to interact with the media wall without contact, including pausing, starting, scrolling forward, scrolling backward, and deleting. This invention innovatively applies face recognition and gesture recognition technologies to building walls, intelligently displaying media wall content and providing a contactless interactive method, giving users not only a sense of presence and surprise, but also a sense of interaction and friendliness.

The invention relates to the technical field of driving behavior safety recognition, in particular to a two-dimensional holder face positioning and fatigue driving detection device which comprises a data collection and storage module, an image information recognition and processing module, a face analysis and fatigue driving evaluation module and an intelligent alarm module. A two-dimensional image lossless storage mechanism is established in the data collection and storage module, the face image information is compressed and stored, and a two-dimensional image storage information base is obtained; a two-dimensional image feature recognition model and a two-dimensional image processing model are arranged in the image information recognition and processing module, a two-dimensional image information base is recognized and processed based on the models, and face monitoring information is obtained; a face analysis model and a fatigue driving evaluation system are established in the face analysis and fatigue driving evaluation module, and the module receives and processes the face monitoring information and outputs a fatigue driving evaluation result; and the intelligent alarm module sends a warning signal according to the evaluation result, so that the fatigue driving state is monitored and alarmed.

The invention is suitable for the technical field of man-machine interaction, and provides an auxiliary makeup method and system based on a user face analysis algorithm, and the method comprises the steps: firstly obtaining the intentional makeup information and multiple pieces of makeup feature information of a target user, and then obtaining the basic face information and real-time face information of the target user, and generating target makeup information based on the basic face information and the makeup feature information, generating expected makeup information based on the real-time face information and the makeup feature information, and finally sending the target makeup information and the expected makeup information to a display screen. According to the method, the user can conveniently know the final effect of the current makeup operation in real time, the user is assisted in forming a clearer expectation for the final makeup, the user can easily compare the current effect with the ideal target makeup, the user is helped to make more appropriate adjustment in the makeup process, the more satisfactory makeup effect is achieved, and the user experience is improved. And the user experience is obviously improved.

The invention relates to an unconditional face image generation method and system based on end-to-end self-distillation, and the method comprises the steps: obtaining a face image, inputting the face image into a variational auto-encoder and a face analysis network, and obtaining potential features and a face region analysis result; constructing an end-to-end self-distillation unconditional generation network, and adding current and previous time step noise to the potential features to obtain temporary strong and weak random Gaussian noise; constructing face region noise based on a face region analysis result, and combining the face region noise with the noise to obtain strong and weak noise; weak noise is input into a target SiT to extract deep representation, strong noise is input into online SiT to extract shallow representation, and loss optimization parameters are aligned through representation; performing face high-order structure clustering on the deep representation to obtain a hypergraph, and calculating a Laplacian matrix as attention structure bias of online SiT; and on-line SiT, strong noise is input to predict noise, and parameters are optimized through diffusion loss. According to the invention, diversified face images can be efficiently generated with high quality, and the structure consistency and detail expression are improved.

The invention relates to the field of face capture, in particular to a VR virtual character interaction system and method based on user-defined expression and mouth shape synchronization, and the system comprises a data collection unit which is used for collecting face data of a target user; the face analysis unit is used for determining a paragraph division mode according to the face analysis state; the paragraph division unit is used for executing a paragraph division The point location processing unit is used for judging whether the sub-region is a target sub-region or not according to the motion trend median value, determining a range setting mode according to the influence coefficient difference degree of the first key point and determining a second key point setting strategy according to the motion state of the first key point; according to the invention, the virtual character interaction effect is improved.

The application provides an occlusion face recognition method based on an occlusion-feature mapping relationship, and the method comprises the following steps: establishing a face analysis and face key point detection module, obtaining a face semantic region division picture through the face analysis module; performing sub-region division on the face semantic region through the face key point detection module; inputting a pair of original face images and sub-region occlusion face images into a differential twin network to learn the mapping relationship between the occlusion and the deep feature, thereby establishing a mask dictionary of different sub-regions of the face; segmenting a binary image of the occlusion region through a segmentation network FCN-8s; merging the corresponding occlusion sub-region mask dictionary through the binary image query; multiplying the face picture to be recognized with the corresponding mask dictionary in the face recognition network to eliminate the damaged feature elements caused by the occlusion, so that the recognition rate of the occlusion face is improved; and the feasibility of the method is verified through experiments on a public data set.

The application discloses a method and device for tracing a fake face based on region classification, a medium and equipment. The method comprises the following steps: acquiring a face picture, wherein the face picture comprises a regionally fake picture, a globally fake picture and a real picture; performing face analysis on the face picture to obtain a face region picture, a background region picture and a connection region picture; performing trace feature extraction on the face picture to obtain a global feature and a global feature decision vector, and performing trace feature extraction on the face region picture, the background region picture and the connection region picture to obtain a face region feature, a background region feature, a connection region feature, a face region feature decision vector, a background region feature decision vector and a connection region feature decision vector; performing feature-level fusion and decision-level fusion on the features and the feature decision vectors to obtain a coarse-grained trace decision vector; and inputting the coarse-grained trace decision vector into a classifier to predict a trace result. The application can improve the accuracy of face tracing.

An object of a system according to an embodiment is to propose optimal makeup to a user and explain a procedure thereof.SOLUTION: A system according to an embodiment includes a face analysis unit, a makeup suggestion unit, a makeup simulation unit, and a makeup procedure explanation unit. The face analysis unit analyzes features of the face of the user. The makeup suggestion unit suggests optimal makeup based on the feature analyzed by the face analysis unit. The makeup simulator applies the makeup proposed by the makeup proposal unit to the face of the user. The makeup procedure explanation unit explains a makeup procedure applied by the makeup simulation unit.SELECTED DRAWING: Figure 1

A face analysis method based on multi-expert features and task prompts comprises the following steps: extracting aggregated multi-expert features from an input image by adopting a unified student network under the supervision of a multi-expert teacher network, generating task features according to task prompts and the aggregated multi-expert features through a feature fusion network, and analyzing the task features according to the task prompts and the aggregated multi-expert features. And finally, a face analysis result is obtained by the task head according to task feature classification. According to the invention, task features are aggregated from a plurality of teachers which are not limited to specific face downstream tasks by using a unified network, and expert features required by each downstream specific task are adaptively selected by using a unified feature fusion network with task prompts. Therefore, face recognition (metric learning), face attribute recognition (classification task) and age estimation (regression task) can be carried out at the same time, and complex and heavy specific task sub-network design is not needed.

The application discloses a multi-task real-time face analysis method and system based on OpenCV and light DNN, and relates to the technical field of computer vision. Input data in the form of static images or real-time video streams is preprocessed through OpenCV; face detection is completed based on a light MobileNet-SSD network optimized through channel pruning and dynamic weight distillation, and effective face images are screened; the face images are input into a multi-task light DNN model, synchronous inference of expression recognition, age prediction and gender classification is realized through a cross-task feature interaction mechanism; real-time video streams are processed through multi-thread parallel computing and optional CUDA GPU acceleration, analysis results are stored in a database in association and visual display and query are supported. The system corresponds to data input, preprocessing, face detection, multi-task analysis, parallel acceleration, data storage and result display and interaction modules. The application balances model precision and computing efficiency, improves complex scene adaptability and real-time response capability, and is suitable for various intelligent interaction scenes.

The application discloses a face restoration method based on a multi-scale face analysis graph, and belongs to the technical field of computer vision, machine learning and the like. First, the application uses an encoder-decoder structure to build a basic network, and adds a skip connection with a channel attention module to the basic network, so that the decoder can fully utilize the effective information contained in the extracted feature map to restore the face. Meanwhile, the face analysis graph is taken as a style image to be fused into the generated face image. Finally, the application adds a loss function that is beneficial to saving identity information during training, and uses the feature layer extracted by the face recognition network to constrain the identity of the restored face, and adds an adversarial loss and a multi-scale discriminator, so that the network can further generate a face image with more real details.

The application discloses a face cross-domain translation method based on multi-view knowledge integration and frequency consistency, which introduces face knowledge in large single-domain data into limited cross-domain data. Specifically, after the input photo is extracted by a face analysis network, the photo is cascaded into a translation network based on a multi-view knowledge integration model. On the other hand, the input photo is extracted by a face recognition network to obtain face identity features, and the cascaded features and the face identity features are used to calculate KL loss, thereby identity consistency supervision is achieved to enhance the high and low frequency consistency between the generated image and the real image. The frequency consistency loss is a general reconstruction loss, which can be flexibly applied to other generation models to improve the robustness of the model to noise.