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公开(公告)号：US10854207B2
公开(公告)日：2020-12-01
申请号：US16231913
申请日：2018-12-24
Inventor： Wei Li , Binghua Qian , Xingming Jin , Ke Li , Fuzhang Wu , Yongjian Wu , Feiyue Huang
Abstract: A method and an apparatus for training a voiceprint recognition system are provided. The method includes obtaining a voice training data set comprising voice segments of users; determining identity vectors of all the voice segments; identifying identity vectors of voice segments of a same user in the determined identity vectors; placing the recognized identity vectors of the same user in the users into one of user categories; and determining an identity vector in the user category as a first identity vector. The method further includes normalizing the first identity vector by using a normalization matrix, a first value being a sum of similarity degrees between the first identity vector in the corresponding category and other identity vectors in the corresponding category; training the normalization matrix, and outputting a training value of the normalization matrix when the normalization matrix maximizes a sum of first values of all the user categories.
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公开(公告)号：US10692503B2
公开(公告)日：2020-06-23
申请号：US15764573
申请日：2017-03-03
Inventor： Xingming Jin , Wei Li , Fangmai Zheng , Fuzhang Wu , Bilei Zhu , Binghua Qian , Ke Li , Yongjian Wu , Feiyue Huang
Abstract: A voice data processing method and apparatus are provided. The method includes obtaining an I-Vector vector of each of voice samples, and determining a target seed sample in the voice samples. A first cosine distance is calculated between an I-Vector vector of the target seed sample and an I-Vector vector of a target remaining voice sample, where the target remaining voice sample is a voice sample other than the target seed sample in the voice samples. A target voice sample is filtered from the voice samples or the target remaining voice sample according to the first cosine distance, to obtain a target voice sample whose first cosine distance is greater than a first threshold.
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公开(公告)号：US10664580B2
公开(公告)日：2020-05-26
申请号：US16100630
申请日：2018-08-10
Inventor： Feiyue Huang , Yongjian Wu , Guofu Tan , Jilin Li , Zhibo Chen , Xiaoqing Liang , Zhiwei Tao , Kejing Zhou , Ke Mei
Abstract: A sign-in method and server based on facial recognition are provided. The method includes: receiving a face image of a sign-in user from a sign-in terminal. According to the face image of the sign-in user, whether a target registration user matching the sign-in user exists in a pre-stored registration set is detected. The registration set includes a face image of at least one registration user. Further, the target registration user is confirmed as signed in successfully if the target registration user exists in the registration set.
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公开(公告)号：US10650259B2
公开(公告)日：2020-05-12
申请号：US15644043
申请日：2017-07-07
Inventor： Chengjie Wang , Jilin Li , Hui Ni , Yongjian Wu , Feiyue Huang
Abstract: The embodiment of the present invention provides a human face recognition method and recognition system. The method includes that: a human face recognition request is acquired, and a statement is randomly generated according to the human face recognition request; audio data and video data returned by a user in response to the statement are acquired; corresponding voice information is acquired according to the audio data; corresponding lip movement information is acquired according to the video data; and when the lip movement information and the voice information satisfy a preset rule, the human face recognition request is permitted. By performing fit goodness matching between the lip movement information and voice information in a video for dynamic human face recognition, an attack by human face recognition with a real photo may be effectively avoided, and higher security is achieved.
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公开(公告)号：US10599913B2
公开(公告)日：2020-03-24
申请号：US16509091
申请日：2019-07-11
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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Patent Agency Ranking
公开(公告)号：US10068128B2
公开(公告)日：2018-09-04
申请号：US15438371
申请日：2017-02-21
Inventor： Chengjie Wang , Jilin Li , Feiyue Huang , Yongjian Wu
Abstract: The present disclosure pertains to the field of image processing technologies and discloses a face key point positioning method and a terminal. The method includes: obtaining a face image; recognizing a face frame in the face image; determining positions of n key points of a target face in the face frame according to the face frame and a first positioning algorithm; performing screening to select, from candidate faces, a similar face whose positions of corresponding key points match the positions of the n key points of the target face; and determining positions of m key points of the similar face selected through screening according to a second positioning algorithm, m being a positive integer. In this way, the problem that positions of key points obtained by a terminal have relatively great deviations in the related technologies is resolved, thereby achieving an effect of improving accuracy of positioned positions of the key points.
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7.发明授权公开(公告)号：US09129395B2
公开(公告)日：2015-09-08
申请号：US14144348
申请日：2013-12-30
Inventor： Yuan Huang , Feiyue Huang , Yongjian Wu , Liqian Dong
Abstract: A method for implementing a graphic rendering engine may be provided. In the method, rendering function information of a first graphic processing interface and a second graphic processing interface may be extracted. The first graphic processing interface and the second graphic processing interface may be encapsulated as a graphic rendering engine interface. Member functions of the graphic rendering engine interface may be defined according to the rendering function information. A rendering function corresponding to the member functions may be implemented by calling the first graphic processing interface or the second graphic processing interface with the graphic rendering engine interface.
Abstract translation: 可以提供用于实现图形呈现引擎的方法。 在该方法中，可以提取第一图形处理界面和第二图形处理界面的渲染功能信息。 第一图形处理界面和第二图形处理界面可以被封装为图形呈现引擎界面。 可以根据渲染功能信息来定义图形渲染引擎接口的成员功能。 可以通过利用图形呈现引擎接口调用第一图形处理界面或第二图形处理界面来实现与成员函数相对应的呈现功能。
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公开(公告)号：US11928893B2
公开(公告)日：2024-03-12
申请号：US17530428
申请日：2021-11-18
Inventor： Donghao Luo , Yabiao Wang , Chenyang Guo , Boyuan Deng , Chengjie Wang , Jilin Li , Feiyue Huang , Yongjian Wu
IPC: G06V20/40 , G06F18/213 , G06T7/246 , G06V40/20 , G06N3/02
CPC classification number: G06V40/20 , G06F18/213 , G06T7/246 , G06N3/02 , G06T2207/20081
Abstract: An action recognition method includes: obtaining original feature submaps of each of temporal frames on a plurality of convolutional channels by using a multi-channel convolutional layer; calculating, by using each of the temporal frames as a target temporal frame, motion information weights of the target temporal frame on the convolutional channels according to original feature submaps of the target temporal frame and original feature submaps of a next temporal frame, and obtaining motion information feature maps of the target temporal frame on the convolutional channels according to the motion information weights; performing temporal convolution on the motion information feature maps of the target temporal frame to obtain temporal motion feature maps of the target temporal frame; and recognizing an action type of a moving object in image data of the target temporal frame according to the temporal motion feature maps of the target temporal frame on the convolutional channels.
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公开(公告)号：US11436739B2
公开(公告)日：2022-09-06
申请号：US16922196
申请日：2020-07-07
Inventor： Yabiao Wang , Yanhao Ge , Zhenye Gan , Yuan Huang , Changyou Deng , Yafeng Zhao , Feiyue Huang , Yongjian Wu , Xiaoming Huang , Xiaolong Liang , Chengjie Wang , Jilin Li
Abstract: This present disclosure describes a video image processing method and apparatus, a computer-readable medium and an electronic device, relating to the field of image processing technologies. The method includes determining, by a device, a target-object region in a current frame in a video. The device includes a memory storing instructions and a processor in communication with the memory. The method also includes determining, by the device, a target-object tracking image in a next frame and corresponding to the target-object region; and sequentially performing, by the device, a plurality of sets of convolution processing on the target-object tracking image to determine a target-object region in the next frame. A quantity of convolutions of a first set of convolution processing in the plurality of sets of convolution processing is less than a quantity of convolutions of any other set of convolution processing.
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公开(公告)号：US10713532B2
公开(公告)日：2020-07-14
申请号：US15925028
申请日：2018-03-19
Inventor： Shouhong Ding , Jilin Li , Chengjie Wang , Feiyue Huang , Yongjian Wu , Guofu Tan
Abstract: The present disclosure discloses an image recognition method and apparatus, and belongs to the field of computer technologies. The method includes: extracting a local binary pattern (LBP) feature vector of a target image; calculating a high-dimensional feature vector of the target image according to the LBP feature vector; obtaining a training matrix, the training matrix being a matrix obtained by training images in an image library by using a joint Bayesian algorithm; and recognizing the target image according to the high-dimensional feature vector of the target image and the training matrix. The image recognition method and apparatus according to the present disclosure may combine LBP algorithm with a joint Bayesian algorithm to perform recognition, thereby improving the accuracy of image recognition.
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