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    Three-dimensional facial recognition method and system
    1.
    发明授权
    Three-dimensional facial recognition method and system 有权

    公开(公告)号：US10198623B2

    公开(公告)日：2019-02-05

    申请号：US15822853

    申请日：2017-11-27

    Applicant: Beijing University of Posts and Telecommunications ,  TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED

    Inventor： Yue Ming ,  Jie Jiang ,  Tingting Liu ,  Juhong Wang

    IPC: G06K9/00 ,  G06K9/46

    Abstract: The present disclosure provides a three-dimensional facial recognition method and system. The method includes: performing pose estimation on an input binocular vision image pair by using a three-dimensional facial reference model, to obtain a pose parameter and a virtual image pair of the three-dimensional facial reference model with respect to the binocular vision image pair; reconstructing a facial depth image of the binocular vision image pair by using the virtual image pair as prior information; detecting, according to the pose parameter, a local grid scale-invariant feature descriptor corresponding to an interest point in the facial depth image; and generating a recognition result of the binocular vision image pair according to the detected local grid scale-invariant feature descriptor and training data having attached category annotations. The present disclosure can reduce computational costs and required storage space.

    Method and apparatus for recognizing character string in image
    4.
    发明授权
    Method and apparatus for recognizing character string in image 有权

    公开(公告)号：US10262241B2

    公开(公告)日：2019-04-16

    申请号：US15721881

    申请日：2017-09-30

    Applicant: Tencent Technology (Shenzhen) Company Limited

    Inventor： Xiang Bai ,  Juhong Wang ,  Tingting Liu ,  Wei Chen ,  Baoguang Shi ,  Cong Yao ,  Pengyuan Lv

    IPC: G06K9/72 ,  G06K9/62 ,  G06N3/04 ,  G06K9/32

    Abstract: Embodiments of this disclosure belong to the field of computer technologies and disclose a method and an apparatus for recognizing a character string in an image. The method includes: according to image data of a pre-stored sample image block including a character string, and based on a convolutional neural network algorithm, constructing an expression of a probability set corresponding to a character string recognition result of the sample image block, the expression of the probability set being constituted by a to-be-determined parameter; based on a training target of maximizing a probability that the character string recognition result determined according to the probability set is the character string included in the pre-stored sample image block, training the plurality of to-be-determined parameters to obtain a training value for each to-be-determined parameter; and when a to-be-recognized target image block is obtained, determining, based on the convolutional neural network algorithm and the training values of the plurality of to-be-determined parameters, a target probability set corresponding to the target image block, and determining, according to the target probability set, a character string recognition result of the target image block. By means of the embodiments of this disclosure, correctness of recognizing a character string can be improved.

    Information processing method and apparatus
    5.
    发明授权
    Information processing method and apparatus 有权

    公开(公告)号：US10235624B2

    公开(公告)日：2019-03-19

    申请号：US15108758

    申请日：2015-06-11

    Applicant: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED

    Inventor： Kang Liu ,  Huaijun Liu ,  Yanxiong Lu ,  Juhong Wang ,  Tingting Liu ,  Liheng Xu ,  Jun Zhao

    IPC: G06N3/08 ,  G06F3/01 ,  G06N3/04 ,  G06F17/27

    Abstract: An information processing method and apparatus, the method including: training a Deep Neural Network (DNN) by using an evaluation object seed, an evaluation term seed and an evaluation relationship seed (101); at a first input layer, connecting vectors corresponding to a candidate evaluation object, a candidate evaluation term and a candidate evaluation relationship to obtain a first input vector (102); at a first hidden layer, compressing the first input vector to obtain a first middle vector, and at a first output layer, decoding the first middle vector to obtain a first output vector (103); and determining a first output vector whose decoding error value is less than a decoding error value threshold, and determining a candidate evaluation object, a candidate evaluation term and a candidate evaluation relationship corresponding to the determined first output vector as first opinion information (104). By use of the technical solution, precision of extracting opinion information from an evaluation text can be enhanced.

    Three-dimensional facial recognition method and system
    7.
    发明授权
    Three-dimensional facial recognition method and system 有权

    公开(公告)号：US09858472B2

    公开(公告)日：2018-01-02

    申请号：US15212410

    申请日：2016-07-18

    Applicant: TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED

    Inventor： Yue Ming ,  Jie Jiang ,  Tingting Liu ,  Juhong Wang

    IPC: G06K9/00 ,  G06K9/46

    CPC classification number: G06K9/00248 ,  G06K9/00281 ,  G06K9/00288 ,  G06K9/4671 ,  G06K9/4676

    Abstract: The present disclosure provides a three-dimensional facial recognition method and system. The method includes: performing pose estimation on an input binocular vision image pair by using a three-dimensional facial reference model, to obtain a pose parameter and a virtual image pair of the three-dimensional facial reference model with respect to the binocular vision image pair; reconstructing a facial depth image of the binocular vision image pair by using the virtual image pair as prior information; detecting, according to the pose parameter, a local grid scale-invariant feature descriptor corresponding to an interest point in the facial depth image; and generating a recognition result of the binocular vision image pair according to the detected local grid scale-invariant feature descriptor and training data having attached category annotations. The present disclosure can reduce computational costs and required storage space.

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