14 results about "Face aging" patented technology

The invention provides a game anti-addiction face age evaluation method and device, and relates to the technical field of artificial intelligence, and the method comprises the steps: in response to a verification request of a game application, collecting a face image of a user through an image collection device of terminal equipment; inputting the face image into a lightweight age evaluation model deployed in the terminal device for reasoning, executing living body detection and age evaluation, and determining a living body detection result and age evaluation data; wherein the lightweight age evaluation model is constructed based on a lightweight convolutional neural network, and an age sensitive feature attention module is configured in the model and is used for dynamically improving an endowed weight for an age-related feature region in the face image; the living body detection process and the age evaluation process are carried out cooperatively; and generating a control instruction for the game application according to the living body detection result and the age evaluation data in combination with a preset local anti-addiction rule base.

The invention relates to a face aging prediction method and device, electronic equipment, a storage medium and a program product. The method comprises the steps of obtaining a face image of a target user, actual life information of the target user and hypothetical life information; performing aging prediction on the target user according to the face image and the actual life information to obtain an aging prediction result of the target user based on actual conditions; and performing aging prediction on the target user according to the face image and the hypothetical life information to obtain an aging prediction result of the target user based on a hypothetical condition.

The application discloses a face image age estimation method based on a global-to-local ordinal regression network. The application comprises the following steps: step 1, preprocessing a data set; step 2, extracting features of face images in the preprocessed data set; step 3, local age domain division; performing k-means clustering analysis on the extracted feature data of the entire data set, and dividing the entire data set into multiple local data domains according to the clustering result; step 4, constructing a global regression network and a local regression network; and step 5, age estimation. The application solves the problem of insufficient precision of an existing deep ordinal regression network in a face age estimation task. Without increasing the complexity of network design, the application effectively improves the accuracy of the ordinal regression network for the face age estimation task. The application has the advantages of high recognition accuracy and strong robustness.

The application provides a fine-grained face age estimation method based on horizontal pyramid matching, and relates to the technical field of image classification. The method is used for face images in different backgrounds. Firstly, the position of the face in the image is detected by using a face detection algorithm. Then, the face image is input into a residual network model Resnet50 to obtain an age feature map. The age feature map is horizontally divided into different pyramid scales to obtain multiple feature slices. Finally, global average pooling and global maximum pooling are respectively performed on each feature slice to obtain the overall age feature and the local age feature of the face, and the face age estimation is completed. The method solves the combination problem of the global feature and the local fine-grained feature of the face age, and realizes the extraction of the overall age feature and the local most recognizable fine-grained feature of the face.

The application discloses a local face age estimation method based on multi-channel learning in a shielding scene and relates to the technical field of age estimation. The application uses a method of simulating shielding, generates partially shielded face images and corresponding shielding labels based on a face age image dataset, trains a shielding detector to detect grids with shielding, proposes an age feature extraction model based on multi-channel learning, divides a to-be-detected image into grids, inputs the to-be-detected image into a feature extraction module, and extracts features of the grids. In combination with the shielding detector result, a feature fusion module is used to fuse features of the grids that are not damaged by shielding, obtain final age features, and further obtain an estimated age value. The application can accurately estimate the face age under a local shielding condition, meets the robustness and accuracy requirements of a face age estimation task in a local shielding scene, and solves the problem that the age estimation is inaccurate due to local shielding in the prior art.

This invention discloses a facial aging level analysis system based on deep metric learning, belonging to the field of intelligent facial image analysis technology. It includes modules for feature mapping, distance calculation, state inference, sequence determination, offset calculation, and result generation. The feature mapping module maps facial depth feature vectors into metric space embedding vectors via a pre-trained deep metric embedding network. The distance calculation module calculates the relative distance between the vectors and preset aging anchor vectors and constructs a distance distribution vector. The state inference module infers the optimal state evolution path based on the aging state transition graph. The sequence determination module determines the aging level and state evolution sequence. The offset calculation module solves for the physiological age offset. The result generation module integrates and outputs the analysis results. This invention achieves refined facial aging level analysis with evolutionary logic through metric space mapping and state evolution inference.

A face aging prediction method and apparatus, an electronic device, a storage medium, and a program product. The method comprises: obtaining a face image of a target user, and real-life information and hypothetical life information of the target user; on the basis of the face image and the real-life information, performing aging prediction on the target user to obtain an aging prediction result of the target user under real conditions; and on the basis of the face image and the hypothetical life information, performing aging prediction on the target user to obtain an aging prediction result of the target user under hypothetical conditions.

The present application relates to the field of medical data processing, in particular to a face aging grade evaluation method and system based on multi-dimensional data, comprising: obtaining a reference age span D1 of historical users in each dimension; obtaining a growth trend of a face data growth sequence based on D1, denoted as M1; obtaining a reference face data span D2 of each dimension for face data distribution of all similar users in each dimension between adjacent reference ages; obtaining an average difference M2 between face data of the current user and face data of the same-age history whose difference with the face data of the current user is greater than D2; and evaluating the face aging grade of the current user by combining M1 and M2 after obtaining the difference between the noise of the face data of all historical users when the face data changes with age under the reference age span and the noise of the face data distribution. The present application ensures the reliability of the face aging evaluation result.

The present application belongs to the field of pattern recognition, and particularly relates to a face age estimation method and system fusing gender and race information, comprising: establishing a face age prediction model fusing gender and race information, including feature mapping and stacking a RepPsconv module into a convolutional neural network, the feature mapping including a main module and an auxiliary module, and the specific steps are: obtaining a face image set with labels of age, gender, race and the like for preprocessing; inputting the preprocessed face image into the convolutional neural network to extract features and fuse them; connecting the fused features to the main module and the auxiliary module, and optimizing the result of the main module by the auxiliary module; solving the loss of the output result of the main module and the label information of the face image, and iteratively training until convergence; inputting a face image to be measured into the trained model, and outputting an age estimation result, and the present application proposes an efficient RepPSconv module, fuses gender and race information, and improves the precision of face age estimation.

The invention is suitable for the field of computer image processing, and provides a face senescence detection method and system, electronic equipment and a computer program product, and the method comprises the steps: obtaining a first three-dimensional mesh model and a second three-dimensional mesh model of a target face; based on a plurality of set facial feature points, aligning the first three-dimensional grid model and the second three-dimensional grid model to the same space coordinate system, and correspondingly establishing a vertex mapping relation in one-to-one correspondence between a plurality of first vertexes and a plurality of second vertexes; calculating a projection difference value of each first vertex relative to the corresponding second vertex; and according to the plurality of projection differences, determining sag information and sag information of the plurality of first vertexes, and correspondingly generating and outputting an aging detection report of the target face. According to the scheme, the recess information and the droop information of the target face can be determined based on the three-dimensional grid model of the target face, the aging detection report of the target face is correspondingly generated, and face aging detection based on the recess feature and the droop feature is realized.

The application provides a face age simulation method, and belongs to the technical field of artificial intelligence. The face age data set used by the face age simulation method is subjected to image preprocessing by using a semantic segmentation model to obtain a face image to be subjected to age simulation. An original-target age difference code required when the original age range is aged to a target age range is designed. A generative adversarial network is constructed, and then age difference information is constructed. Network parameters are updated in a reverse propagation manner, and then the training of the generative adversarial network is completed. The face image to be aged is taken as the input of the generator to obtain a face age simulation image. The face age simulation image obtained based on the age difference information can make the training time required for generating a clear age simulation face effect shorter, and the identity consistency performance of the generated age simulation face better, so that the obtained age simulation face is closer to the real face appearance of the input face at the target age range.

The embodiment of the invention provides a face age synthesis method based on hierarchical causal learning. The method is applied to the technical field of computer vision, and comprises the following steps: performing key point extraction on a to-be-edited face image, and dividing the face image into a plurality of areas according to the extracted key points; performing feature extraction on each region by using a visual encoder to obtain an attribute feature set; determining a causal relationship among the different attribute features, and constructing a causal graph according to the causal relationship among the different attribute features; using a cross attention mechanism to fuse the age features and the causal graph information, and for each causal edge, distinguishing high-level attributes and low-level attributes according to an attribute hierarchical relationship; performing nonlinear editing on the low-level attribute to obtain an edited low-level attribute value, and performing linear editing on the high-level attribute to obtain an edited high-level attribute value; and inputting the edited low-level attribute value and the edited high-level attribute value into a decoder for analysis and processing to obtain an edited face image, thereby improving the accuracy of the generated image.

The invention relates to an auxiliary attribute-guided multi-scale feature fusion face age estimation method, and aims to solve the problems that fine-grained aging features are insufficient to capture, global and local single-scale features are difficult to balance, and noise is easy to introduce by an auxiliary attribute fixed weight in an existing method. The method comprises the following steps: preprocessing a face data set containing age labels, genders and crowd attribute labels, extracting multi-scale features through ResNet18, enhancing the features through deformable convolution and an attention mechanism, and integrating multi-scale information by adopting a progressive fusion strategy; sub-age estimation branches corresponding to attribute combinations are constructed, branch weights are dynamically calculated in combination with an adaptive weight module, and the model is optimized through a joint loss function. According to the method, the accuracy and the cross-domain robustness of age estimation are effectively improved, the influence of redundant features and data deviation is reduced, and the method is adaptive to multiple scenes such as medical diagnosis, public safety and intelligent service and has good practicability and economic benefits.

The application provides a face age estimation method based on a hybrid expert network, which can be applied to the technical field of computer vision. The method comprises the following steps: performing face cutting processing on face data to be identified to generate a pretreatment image, and then inputting the pretreatment image into a feature extraction network to obtain original features; inputting the original features into a feature extraction network based on a full connection layer to obtain global features and a feature extraction network based on a hybrid expert network to obtain local features; then adaptively fusing the extracted global features and local features; finally, inputting the fused features into an output layer to obtain a prediction result of face age. The face age estimation method based on the hybrid expert network can adaptively obtain global and local features of face age, and improve the accuracy of face age estimation.