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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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公开(公告)号：US10438329B2
公开(公告)日：2019-10-08
申请号：US15699691
申请日：2017-09-08
Abstract: The method provided in the present disclosure includes: obtaining an image photographed by a camera, and performing face detection on the image by using a face detection algorithm, to obtain a face pixel set from the image; positioning a facial feature contour mask over the face pixel set, to obtain a to-be-examined pixel set from the face pixel set, the to-be-examined pixel set including: a plurality of pixels within an image area except pixels masked by the facial feature contour mask in the face pixel set; performing edge contour detection on the to-be-examined pixel set, and extracting one or more blemish regions from the to-be-examined pixel set, to obtain a to-be-retouched pixel set, the to-be-retouched pixel set including: a plurality of pixels within an image area belonging to the blemish regions; and retouching all pixels in the to-be-retouched pixel set, to obtain a retouched pixel set.
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公开(公告)号：US10432624B2
公开(公告)日：2019-10-01
申请号：US15632143
申请日：2017-06-23
Inventor： Feiyue Huang , Jilin Li , Guofu Tan , Xiaoli Jiang , Dan Wu , Junwu Chen , Jianguo Xie , Wei Guo , Yihui Liu , Jiandong Xie
Abstract: An identity verification method performed at a terminal includes: displaying and/or playing in an audio form action guide information selected from a preset action guide information library, and collecting a corresponding set of action images within a preset time window; performing matching detection on the collected set of action images and the action guide information, to obtain a living body detection result indicating whether a living body exists in the collected set of action images; according to the living body detection result that indicates that a living body exists in the collected set of action images: collecting user identity information and performing verification according to the collected user identity information, to obtain a user identity information verification result; and determining the identity verification result according to the user identity information verification result.
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公开(公告)号：US20190138791A1
公开(公告)日：2019-05-09
申请号：US16222941
申请日：2018-12-17
Inventor： Chengjie WANG , Jilin Li , Yandan Zhao , Hui Ni , Yabiao Wang , Ling Zhao
Abstract: When a target image is captured, the device provides a portion of the target image within a target detection region to a preset first model set to calculate positions of face key points and a first confidence value. The face key points and the first confidence value are output by the first model set for a single input of the portion of the first target image into the first model set. When the first confidence value meets a first threshold corresponding to whether the target image is a face image, the device obtains a second target image corresponding to the positions of the first face key points; the device inputs the second target image into the first model set to calculate a second confidence value, the second confidence value corresponds to accuracy key point positioning, and outputs the first key points if the second confidence value meets a second threshold.
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公开(公告)号：US10055879B2
公开(公告)日：2018-08-21
申请号：US15652009
申请日：2017-07-17
Inventor： Chengjie Wang , Jilin Li , Feiyue Huang , Lei Zhang
CPC classification number: G06T15/10 , G06K9/00214 , G06K9/00281 , G06T7/73 , G06T15/04 , G06T19/20 , G06T2207/30201 , G06T2210/44 , G06T2215/16 , G06T2219/2021
Abstract: A 3D human face reconstruction method and apparatus, and a server are provided. In some embodiments, the method includes determining feature points on an acquired 2D human face image; determining posture parameters of a human face according to the feature points, and adjusting a posture of a universal 3D human face model according to the posture parameters; determining points on the universal 3D human face model corresponding to the feature points, and adjusting the corresponding points in a sheltered status to obtain a preliminary 3D human face model; and performing deformation adjusting on the preliminary 3D human face model, and performing texture mapping on the deformed 3D human face model to obtain a final 3D human face.
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Patent Agency Ranking
公开(公告)号：US20180204094A1
公开(公告)日：2018-07-19
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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公开(公告)号：US12131580B2
公开(公告)日：2024-10-29
申请号：US17733968
申请日：2022-04-29
Inventor： Jian Li , Bin Zhang , Yabiao Wang , Jinlong Peng , Chengjie Wang , Jilin Li , Feiyue Huang , Yongjian Wu
CPC classification number: G06V40/164 , G06N3/08 , G06V10/806 , G06V40/168
Abstract: A face detection method includes: acquiring a target image; invoking a face detection network, and processing the target image by using a feature extraction structure of the face detection network, to obtain original feature maps corresponding to the target image; the original feature maps having different resolutions; processing the original feature maps by using a feature enhancement structure of the face detection network, to obtain an enhanced feature map corresponding to each original feature map; the feature enhancement structure being obtained by searching a search space, and the search space used for searching the feature enhancement structure being determined based on a detection objective of the face detection network and a processing object of the feature enhancement structure; and processing the enhanced feature map by using a detection structure of the face detection network, to obtain a face detection result of the target image.
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公开(公告)号：US11972638B2
公开(公告)日：2024-04-30
申请号：US17513731
申请日：2021-10-28
Inventor： Jian Zhang , Jia Meng , Taiping Yao , Ying Tai , Shouhong Ding , Jilin Li
CPC classification number: G06V40/45 , G06V10/56 , G06V20/64 , G06V40/171 , G06V40/172 , G06V10/95
Abstract: This application provides a face living body detection method performed by a computing device, the method including: obtaining a first face image of a target detection object in a first illumination condition and a second face image of the target detection object in a second illumination condition, determining a difference image according to the two images, decoupling an object reflectivity and an object normal vector corresponding to the target detection object from a feature map extracted from the difference image, and determining whether the target detection object is a living body according to the object reflectivity and the object normal vector. This method decouples texture information and depth information of a face, and performs living body detection by using decoupled information, which increases the defense capability against 3D attacks, thereby effectively defending against planar attacks and 3D attacks.
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公开(公告)号：US11275932B2
公开(公告)日：2022-03-15
申请号：US16938858
申请日：2020-07-24
Inventor： Siqian Yang , Jilin Li , Yongjian Wu , Yichao Yan , Keke He , Yanhao Ge , Feiyue Huang , Chengjie Wang
Abstract: This application discloses a human attribute recognition method performed at a computing device. The method includes: determining a human body region image in a surveillance image; inputting the human body region image into a multi-attribute convolutional neural network model, to obtain, for each of a plurality of human attributes in the human body region image, a probability that the human attribute corresponds to a respective predefined attribute value, the multi-attribute convolutional neural network model being obtained by performing multi-attribute recognition and training on a set of pre-obtained training images by using a multi-attribute convolutional neural network; determining, for each of the plurality of human attributes in the human body region image, the attribute value of the human attribute based on the corresponding probability; and displaying the attribute values of the plurality of human attributes next to the human body region image.
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公开(公告)号：US11200404B2
公开(公告)日：2021-12-14
申请号：US17089435
申请日：2020-11-04
Inventor： Yandan Zhao , Yichao Yan , Weijian Cao , Yun Cao , Yanhao Ge , Chengjie Wang , Jilin Li
Abstract: This application relates to feature point positioning technologies. The technologies involve positioning a target area in a current image; determining an image feature difference between a target area in a reference image and the target area in the current image, the reference image being a frame of image that is processed before the current image and that includes the target area; determining a target figure point location of the target area in the reference image; determining a target feature point location difference between the target area in the reference image and the target area in the current image according to a feature point location difference determining model and the image feature difference; and positioning a target feature point in the target area in the current image according to the target feature point location of the target area in the reference image and the target feature point location difference.
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