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Patent Agency Ranking
公开(公告)号：US10664693B2
公开(公告)日：2020-05-26
申请号：US15950929
申请日：2018-04-11
Inventor： Feiyue Huang , Jilin Li , Chengjie Wang
Abstract: Aspects of the disclosure provide a method for adding a target contact to a user's friend list in a social network. A target image of a human body part of the target contact can be received from a user terminal. A target biological feature can be extracted from the target image. Whether the target biological feature matches a reference biological feature of a plurality of prestored reference biological features can be determined. A social account associated with the determined reference biological feature that matches the target biological feature may be determined, and added to the user's friend list.
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公开(公告)号：US20200057883A1
公开(公告)日：2020-02-20
申请号：US16665060
申请日：2019-10-28
Inventor： Yanhao Ge , Jilin Li , Chengjie Wang
Abstract: A face attribute recognition method, electronic device, and storage medium. The method may include obtaining a face image, inputting the face image into an attribute recognition model, performing a forward calculation on the face image using the attribute recognition model to obtain a plurality of attribute values according to different types of attributes, and outputting the plurality of attribute values, the plurality of attribute values indicating recognition results of a plurality of attributes of the face image. The attribute recognition model may be obtained through training based on a plurality of sample face images, a plurality of sample attribute recognition results of the plurality of sample face images, and the different types of attributes.
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公开(公告)号：US10438077B2
公开(公告)日：2019-10-08
申请号：US15728178
申请日：2017-10-09
Inventor： Chengjie Wang , Jilin Li , Feiyue Huang , Yongjian Wu
Abstract: A face liveness detection method includes outputting a prompt to complete one or more specified actions in sequence within a specified time period, obtaining a face video, detecting a reference face image frame in the face video using a face detection method, locating a facial keypoint in the reference face image frame, tracking the facial keypoint in one or more subsequent face image frames, determining a state parameter of one of the one or more specified actions using a continuity analysis method according to the facial keypoint, and determining whether the one of the one or more specified actions is completed according to a continuity of the state parameter.
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公开(公告)号：US10410053B2
公开(公告)日：2019-09-10
申请号：US15715579
申请日：2017-09-26
Inventor： Chengjie Wang , Hui Ni , Jilin Li
Abstract: A method for detecting an information card in an image is provided. The method includes performing a line detection to obtain two endpoints of a line segment corresponding to each of four sides of the information card; generating, a linear equation of the side; obtaining coordinates of four intersection points of the four sides of the information card; mapping the coordinates of the four intersection points to four corners of a rectangular box of the information card, to obtain a perspective transformation matrix; performing perspective transformation on image content encircled by four straight lines represented by the four linear equations to provide transformed image content; forming a gradient template according to a layout of information content on the information card; and using the gradient template to match with the transformed image content and determining whether the image content is a correct information card.
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公开(公告)号：US10395095B2
公开(公告)日：2019-08-27
申请号：US15703826
申请日：2017-09-13
Inventor： Shouhong Ding , Jilin Li , Chengjie Wang , Feiyue Huang , Yongjian Wu , Guofu Tan
Abstract: Face model matrix training method, apparatus, and storage medium are provided. The method includes: obtaining a face image library, the face image library including k groups of face images, and each group of face images including at least one face image of at least one person, k>2, and k being an integer; separately parsing each group of the k groups of face images, and calculating a first matrix and a second matrix according to parsing results, the first matrix being an intra-group covariance matrix of facial features of each group of face images, and the second matrix being an inter-group covariance matrix of facial features of the k groups of face images; and training face model matrices according to the first matrix and the second matrix.
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