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    Face Augmentation in Video
    1.
    发明申请
    Face Augmentation in Video 有权

    公开(公告)号：US20220020226A1

    公开(公告)日：2022-01-20

    申请号：US17388946

    申请日：2021-07-29

    Applicant: Intel Corporation

    Inventor： Ke Chen ,  Zhipin Deng ,  Xiaoxia Cai ,  Chen Wang ,  Ya-Ti Peng ,  Yi-Jen Chiu ,  Lidong Xu

    IPC: G06T19/20 ,  G06T7/90 ,  G06K9/00 ,  G06T5/00 ,  G06T15/00

    Abstract: Systems, apparatus, articles of manufacture and methods for face augmentation in video are disclosed. An example apparatus includes executable code to detect a face of a subject in the video, detect a gender of the subject based on the face, detect a skin tone of the subject based on the face, apply a first process to smooth skin on the face in the video, apply a second process to change the skin tone of the face, apply a third process to slim the face, apply a fourth process to adjust a size of eyes on the face, and apply a fifth process to remove an eye bag from the face. One or more of the first process, the second process, the third process, the fourth process, or the fifth process adjustable based on one or more of the gender or an age. The example apparatus also includes one or more processors to generate modified video with beauty effects, the beauty effects based on one or more of the first process, the second process, the third process, the fourth process, or the fifth process.

    LOW-COST FACE RECOGNITION USING GAUSSIAN RECEPTIVE FIELD FEATURES
    6.
    发明申请
    LOW-COST FACE RECOGNITION USING GAUSSIAN RECEPTIVE FIELD FEATURES 审中-公开

    公开(公告)号：US20180082107A1

    公开(公告)日：2018-03-22

    申请号：US15562133

    申请日：2015-03-27

    Applicant: Intel Corporation

    Inventor： Jianguo Li ,  Yurong Chen ,  Ke Chen ,  Ye-Jen Chiu

    IPC: G06K9/00 ,  G06K9/62 ,  G06K9/46

    CPC classification number: G06K9/00281 ,  G06K9/00288 ,  G06K9/4619 ,  G06K9/6228 ,  G06K9/623

    Abstract: Methods and systems may provide for facial recognition of at least one input image utilizing hierarchical feature learning and pair-wise classification. Receptive field theory may be used on the input image to generate a pre-processed multi-channel image. Channels in the pre-processed image may be activated based on the amount of feature rich details within the channels. Similarly, local patches may be activated based on the discriminant features within the local patches. Features may be extracted from the local patches and the most discriminant features may be selected in order to perform feature matching on pair sets. The system may utilize patch feature pooling, pair-wise matching, and large-scale training in order to quickly and accurately perform facial recognition at a low cost for both system memory and computation.

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