Learning model for salient facial region detection

    公开(公告)号:US10579860B2

    公开(公告)日:2020-03-03

    申请号:US15449266

    申请日:2017-03-03

    Abstract: One embodiment provides a method comprising receiving a first input image and a second input image. Each input image comprises a facial image of an individual. For each input image, a first set of facial regions of the facial image is distinguished from a second set of facial regions of the facial image based on a learning based model. The first set of facial regions comprises age-invariant facial features, and the second set of facial regions comprises age-sensitive facial features. The method further comprises determining whether the first input image and the second input images comprise facial images of the same individual by performing face verification based on the first set of facial regions of each input image.

    3-DIMENSIONAL MODEL GENERATION USING EDGES
    3.
    发明申请
    3-DIMENSIONAL MODEL GENERATION USING EDGES 有权
    使用边缘的三维模型生成

    公开(公告)号:US20160098858A1

    公开(公告)日:2016-04-07

    申请号:US14725938

    申请日:2015-05-29

    Abstract: 3-dimensional model generation using edges may include detecting, using a processor, a plurality of edges in a plurality of images and determining, using the processor, a set edges from the plurality of edges that are matched across the plurality of images. Camera poses of the plurality of images may be estimated using the processor and using a cost function that depends upon the set of edges.

    Abstract translation: 使用边缘的三维模型生成可以包括使用处理器来检测多个图像中的多个边缘,并且使用处理器来确定来自跨越多个图像匹配的多个边缘的集合边缘。 可以使用处理器来估计多个图像的摄像机姿态,并且使用取决于该组边缘的成本函数。

    LEARNING MODEL FOR SALIENT FACIAL REGION DETECTION

    公开(公告)号:US20170351905A1

    公开(公告)日:2017-12-07

    申请号:US15449266

    申请日:2017-03-03

    Abstract: One embodiment provides a method comprising receiving a first input image and a second input image. Each input image comprises a facial image of an individual. For each input image, a first set of facial regions of the facial image is distinguished from a second set of facial regions of the facial image based on a learning based model. The first set of facial regions comprises age-invariant facial features, and the second set of facial regions comprises age-sensitive facial features. The method further comprises determining whether the first input image and the second input images comprise facial images of the same individual by performing face verification based on the first set of facial regions of each input image.

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