Tencent Technology (shenzhen) Company Limited

Tsinghua University

Tencent Technology Company Limited

Ruixin Zhang

Feiyue Huang

Jilin Li

Xiaowei Guo

Shaoxin Li

Yuge Huang

Hui Ni

Chengjie Wang

Bin Hua Liu

Yi Fan Hu

Yongjian Wu

Juhong Wang

Guofu Tan

Xinyang Jiang

Bin Liu

Shimin Hu

Kun Xu

Yi Hu

Xing Sun

Lei Shen

Yingyi Zhang

Xingyu Chen

Fuzhao Ou

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 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

An image processing method is provided, including: obtaining a target image; invoking an image recognition model including: a backbone network, a pooling module and a dilated convolution module that are connected to the backbone network and that are parallel to each other, and a fusion module connected to the pooling module and the dilated convolution module; performing feature extraction on the target image by extracting, using the backbone network, a feature map of the target image, separately processing, using the pooling module and the dilated convolution module, the feature map, to obtain a first result outputted by the pooling module and a second result outputted by the dilated convolution module, and fusing the first result and the second result by using the fusion module into a model recognition result of the target image; and determining a semantic segmentation labeled image of the target image based on the model recognition result.

Image processing method and device, computer apparatus, and storage medium

The present disclosure describes method, apparatus, and storage medium for obtaining object information. The method includes obtaining a to-be-tracked image comprising at least one object and at least one reference image comprising a plurality of objects; extracting a to-be-tracked image block comprising a plurality of to-be-tracked points from the to-be-tracked image and extracting a reference image block comprising a plurality of reference points from a reference image of the at least one reference image; constructing a point transformation relationship between the to-be-tracked image block and the reference image block based on a position relationship between the plurality of to-be-tracked points and a position relationship between the plurality of reference points; and obtaining a position of a reference point in the reference image corresponding to a to-be-tracked point based on the point transformation relationship, to determine an object in the reference image corresponding to the at least one object.

Method, apparatus, and storage medium for obtaining object information

The present disclosure discloses a living body identification method, an information generation method, and a terminal, and belongs to the field of biometric feature recognition. The method includes: providing lip language prompt information, the lip language prompt information including at least two target characters, and the at least two target characters being at least one of: characters of a same lip shape, characters of opposite lip shapes, or characters whose lip shape similarity is in a preset range; collecting at least two frame pictures; detecting whether lip changes of a to-be-identified object in the at least two frame pictures meet a preset condition, when the to-be-identified object reads the at least two target characters; and determining that the to-be-identified object is a living body, if the preset condition is met. The present disclosure resolves a problem in the related technology that even if a to-be-identified object performs an operation according to lip language prompt information, a terminal may incorrectly determine that the to-be-identified object is not a living body, and achieves an effect that the terminal can accurately determine whether the to-be-identified object is a living body and improve determining accuracy.

Living body identification method, information generation method, and terminal

An image classification method is provided. The method includes: inputting a to-be-classified image into a plurality of neural network models; obtaining data output by multiple non-input layers specified by each neural network model to generate a plurality of image features corresponding to the plurality of neural network models; respectively inputting the plurality of corresponding image features into linear classifiers, each of the linear classifiers being trained by one of the plurality of neural network models for determining whether an image belongs to a preset class; obtaining, using each neural network model, a corresponding probability that the to-be-classified image comprises an object image of the preset class; and determining, according to each obtained probability, whether the to-be-classified image includes the object image of the preset class.

Image classification method, electronic device, and storage medium

Growing community of inventors

Shenzhen, China

Ruixin Zhang