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公开(公告)号：US11816880B2
公开(公告)日：2023-11-14
申请号：US17744260
申请日：2022-05-13
Inventor： Jianqing Xu , Pengcheng Shen , Shaoxin Li
IPC: G06V10/75 , G06V40/16 , G06V10/77 , G06V10/74 , G06V10/774 , G06V10/776
CPC classification number: G06V10/751 , G06V10/761 , G06V10/774 , G06V10/776 , G06V10/7715 , G06V40/168 , G06V40/172
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.
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Patent Agency Ranking
公开(公告)号：US10817708B2
公开(公告)日：2020-10-27
申请号：US16297565
申请日：2019-03-08
Inventor： Chengjie Wang , Hui Ni , Yandan Zhao , Yabiao Wang , Shouhong Ding , Shaoxin Li , Ling Zhao , Jilin Li , Yongjian Wu , Feiyue Huang , Yicong Liang
IPC: G06K9/00
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.
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公开(公告)号：US10909356B2
公开(公告)日：2021-02-02
申请号：US16356924
申请日：2019-03-18
Inventor： Yicong Liang , Chengjie Wang , Shaoxin Li , Yandan Zhao , Jilin Li
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.
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4.发明申请公开(公告)号：US20200342214A1
公开(公告)日：2020-10-29
申请号：US16927812
申请日：2020-07-13
Inventor： Anping LI , Shaoxin Li , Chao Chen , Pengchen Shen , Jilin Li
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.
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公开(公告)号：US11436435B2
公开(公告)日：2022-09-06
申请号：US16985170
申请日：2020-08-04
Inventor： Anping Li , Shaoxin Li , Chao Chen , Pengcheng Shen , Shuang Wu , Jilin Li
Abstract: 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.
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公开(公告)号：US11335124B2
公开(公告)日：2022-05-17
申请号：US16927812
申请日：2020-07-13
Inventor： Anping Li , Shaoxin Li , Chao Chen , Pengcheng Shen , Jilin Li
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.
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