Abstract: A face recognition method includes: obtaining a first feature image that describes a face feature of a target face image and a first feature vector corresponding to the first feature image; obtaining a first feature value that represents a degree of difference between a face feature in the first feature image and that in the target face image; obtaining a similarity between the target face image and a template face image according to the first feature vector, the first feature value, and a second feature vector and a second feature value corresponding to a second feature image of the template face image, the second feature value describing a degree of difference between a face feature in the second feature image and that in the template face image; and determining, when the similarity is greater than a preset threshold, that the target face image matches the template face image.

Abstract: Embodiments of this application disclose a face recognition method and apparatus, an electronic device and a storage medium. The embodiments of this application can obtain a face image feature of a resource transfer object and a device identification of a resource transfer device; search for a target face feature matching the face image feature, and determine a target object corresponding to the target face feature; search for a resource transfer device graph feature corresponding to the device identification and a target object graph feature corresponding to the target object; determine an initial resource transfer probability that the target object performs resource transfer at the resource transfer place; generate a fused resource transfer probability according to a similarity between the face image feature and the target face feature and the initial resource transfer probability; and determine a resource transfer verification manner of the resource transfer object.

Abstract: Embodiments of this application provide a data processing method and apparatus, a device, and a medium.

Abstract: A data processing method is provided. In the method, a first model that includes N network layers is obtained. The first model is trained with a first data set that includes first data and training label information of the first data, N being a positive integer. The first model is trained with a second data set. The second data set including second data and training label information of the second data, the second data being quantized. A first unquantized target network layer of the N network layers is quantized. Further, an updated first model that includes the quantized first target network layer is trained with the second data set to obtain a second model.

Abstract: The present disclosure relates to data processing method and apparatus. The method includes: acquiring local model parameters respectively corresponding to N local recognition models; acquiring M parameter fusion modes associated with a local model parameter set, and performing parameter fusion on the local model parameter set respectively according to each parameter fusion mode, so as to obtain M alternative global models; and acquiring evaluation indexes of the M alternative global models respectively in a multimedia verification data set, determining a target global model in the M alternative global models according to the evaluation indexes, and transmitting the target global model to N clients, the N clients updating parameters of a local recognition model associated with the target global model according to the target global model respectively, so as to obtain an object recognition model.

Abstract: A face image quality assessment method includes acquiring a face image, and determining a similarity distribution distance of the face image, the similarity distribution distance including a distribution distance between an intra-person similarity distribution of the face image and an inter-person similarity distribution of the face image. The intra-person similarity distribution indicates a similarity distribution between the face image and a first-type image of a same person as the face image, and the inter-person similarity distribution indicates a similarity distribution between the face image and a second-type image of a different person from the face image. The method further includes determining image quality of the face image based on the similarity distribution distance of the face image.

Abstract: This application discloses a method and an apparatus for selecting a face image, a device, and a storage medium and relates to the field of artificial intelligence technologies. The method includes detecting, after a frame of face image is obtained, whether the face image meets a preliminary quality screening condition; determining, in response to a first face image meeting the preliminary quality screening condition, an overall quality score of the first face image, the overall quality score representing overall quality of the face image; and transmitting the first face image to a face recognition process in response to the overall quality score of the first face image being greater than a level-one threshold.

Abstract: This application relates to a model training method.

Abstract: A face recognition method includes: obtaining a first feature image that describes a face feature of a target face image and a first feature vector corresponding to the first feature image; obtaining a first feature value that represents a degree of difference between a face feature in the first feature image and that in the target face image; obtaining a similarity between the target face image and a template face image according to the first feature vector, the first feature value, and a second feature vector and a second feature value corresponding to a second feature image of the template face image, the second feature value describing a degree of difference between a face feature in the second feature image and that in the template face image; and determining, when the similarity is greater than a preset threshold, that the target face image matches the template face image.

Abstract: Embodiments of this application disclose a palm print recognition method, a method for training a feature extraction model, a device, and a storage medium, and belong to the field of computer technologies. The method includes: obtaining sample hand images for each of a plurality of sample user identifiers, at least two of the sample hand images associated with the each of the plurality of sample user identifier being acquired from image acquisition devices having different image acquisition characteristics; calling a feature extraction model to extract sample palm print features of the plurality of sample user identifiers; and adjusting the feature extraction model according to the sample palm print features of the plurality of sample user identifiers.

Abstract: This application relates to an image processing method and apparatus, a facial recognition method and apparatus, a computer device, and a readable storage medium.

Abstract: This application relates to a face recognition method performed at a computer server. After obtaining a to-be-recognized face image, the server inputs the to-be-recognized face image into a classification model. The server then obtains a recognition result of the to-be-recognized face image through the classification model. The classification model is obtained by inputting a training sample marked with class information into the classification model, outputting an output result of the training sample, calculating a loss of the classification model in a training process according to the output result, the class information and model parameters of the classification model, and performing back propagation optimization on the classification model according to the loss.

Abstract: A facial tracking method can include receiving a first vector of a first frame, and second vectors of second frames that are prior to the first frame in a video. The first vector is formed by coordinates of first facial feature points in the first frame and determined based on a facial registration method. Each second vector is formed by coordinates of second facial feature points in the respective second frame and previously determined based on the facial tracking method. A second vector of the first frame is determined according to a fitting function based on the second vectors of the first set of second frames. The fitting function has a set of coefficients that are determined by solving a problem of minimizing a function formulated based on a difference between the second vector and the first vector of the current frame, and a square sum of the coefficients.

Abstract: This application relates to an image processing method and apparatus, a facial recognition method and apparatus, a computer device, and a readable storage medium.

Abstract: This application relates to a model training method.

Abstract: This application relates to a face recognition method performed at a computer server. After obtaining a to-be-recognized face image, the server inputs the to-be-recognized face image into a classification model. The server then obtains a recognition result of the to-be-recognized face image through the classification model. The classification model is obtained by inputting a training sample marked with class information into the classification model, outputting an output result of the training sample, calculating a loss of the classification model in a training process according to the output result, the class information and model parameters of the classification model, and performing back propagation optimization on the classification model according to the loss.

Abstract: A facial tracking method is provided. The method includes: obtaining, from a video stream, an image that currently needs to be processed as a current image frame; and obtaining coordinates of facial key points in a previous image frame and a confidence level corresponding to the previous image frame. The method also includes calculating coordinates of facial key points in the current image frame according to the coordinates of the facial key points in the previous image frame when the confidence level is higher than a preset threshold; and performing multi-face recognition on the current image frame according to the coordinates of the facial key points in the current image frame. The method also includes calculating a confidence level of the coordinates of the facial key points in the current image frame, and returning to process a next frame until recognition on all image frames is completed.

Abstract: A facial tracking method can include receiving a first vector of a first frame, and second vectors of second frames that are prior to the first frame in a video. The first vector is formed by coordinates of first facial feature points in the first frame and determined based on a facial registration method. Each second vector is formed by coordinates of second facial feature points in the respective second frame and previously determined based on the facial tracking method. A second vector of the first frame is determined according to a fitting function based on the second vectors of the first set of second frames. The fitting function has a set of coefficients that are determined by solving a problem of minimizing a function formulated based on a difference between the second vector and the first vector of the current frame, and a square sum of the coefficients.

Abstract: A facial tracking method is provided. The method includes: obtaining, from a video stream, an image that currently needs to be processed as a current image frame; and obtaining coordinates of facial key points in a previous image frame and a confidence level corresponding to the previous image frame. The method also includes calculating coordinates of facial key points in the current image frame according to the coordinates of the facial key points in the previous image frame when the confidence level is higher than a preset threshold; and performing multi-face recognition on the current image frame according to the coordinates of the facial key points in the current image frame. The method also includes calculating a confidence level of the coordinates of the facial key points in the current image frame, and returning to process a next frame until recognition on all image frames is completed.