Tencent Technology (shenzhen) Company Limited

Tencent Technology Company Limited

Shaoxin Li

Jilin Li

Pengcheng Shen

Chengjie Wang

Chao Xi Chen

Feiyue Huang

Shouhong Ding

Yandan Zhao

Ruixin Zhang

Yicong Liang

Anping Li

Yuge Huang

Hui Ni

Hui Li

Yongjian Wu

Shuang Wu

Ying Tai

Yabiao Wang

Yun Cao

Ling Zhao

Lei Shen

Jianqing Xu

Yingyi Zhang

Xingyu Chen

Fuzhao Ou

Jiaxin Gu

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.

Assessing face image quality for application of facial recognition

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.

Face recognition method, apparatus, electronic device, and storage medium

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.

Palm print recognition method, method for training feature extraction model, comprising obtaining sample hand images for user identifiers, calling feature extraction model to extract palm print features, and adjust the feature extraction model according to the sample palm print features

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.

Face recognition method and apparatus, computer device, and storage medium

This application relates to a model training method. The method includes retrieving a current group of training samples, the training samples being based on a training set; obtaining first sample features of training samples in the current group of training samples based on a to-be-trained model; and obtaining, center features respectively corresponding to the training samples; obtaining feature distribution parameters corresponding to the training samples, the feature distribution parameter corresponding to each training sample being obtained by collecting statistics on second sample features of training samples in the training set that belong to the same classification category, and the second sample feature of each training sample being generated by a trained model; obtaining, based on the center features and the feature distribution parameters, a comprehensive loss parameter corresponding to the current group of training samples; and adjusting model parameters of the to-be-trained model based on the comprehensive loss parameter.

Model training method, storage medium, and computer device

This application relates to an image processing method and apparatus, a facial recognition method and apparatus, a computer device, and a readable storage medium. The image processing method includes: obtaining a target image comprising an object wearing glasses; inputting the target image to a glasses-removing model comprising a plurality of sequentially connected convolution squeeze and excitation networks; obtaining feature maps of feature channels of the target image through convolution layers of the convolution squeeze and excitation networks; obtaining global information of the feature channels according to the feature maps through squeeze and excitation layers of the convolution squeeze and excitation networks, learning the global information, and generating weights of the feature channels; weighting the feature maps of the feature channels according to the weights through weighting layers of the convolution squeeze and excitation networks, respectively, and generating weighted feature maps; and generating a glasses-removed image corresponding to the target image according to the weighted feature maps through the glasses-removing model. The glasses in the image can be effectively removed using the method.

Image processing method and apparatus, facial recognition method and apparatus, and computer device

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.

Face recognition method and apparatus, classification model training method and apparatus, storage medium and computer device

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.

Facial tracking method and apparatus, storage medium, and electronic device

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.

Facial tracking method and apparatus, and storage medium

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Shenzhen, China

Shaoxin Li