首页> 外国专利> HUMAN FACE RECOGNITION METHOD AND APPARATUS COMBINED WITH DEPTH INFORMATION, AND STORAGE MEDIUM

HUMAN FACE RECOGNITION METHOD AND APPARATUS COMBINED WITH DEPTH INFORMATION, AND STORAGE MEDIUM

机译:结合深度信息和存储介质的人脸识别方法和装置

摘要

Disclosed is a human face recognition method combined with depth information. The method comprises: establishing a human face image sample library, wherein a human face ID, a human face color image and a human face depth image match each other in same; constructing and training a human face classification and recognition model to obtain a feature vector of a human face sample image; acquiring a human face image of a target to be recognized, the human face image comprising a human face color image of the target to be recognized and a human face depth image of the target to be recognized; inputting the human face image of the target to be recognized into the human face classification and recognition model, and extracting a feature vector of the human face image of the target to be recognized; and determining, according to the feature vector, a human face ID corresponding to the human face image of the target to be recognized. In combination with depth information about a human face, by means of calculating the vector distance between feature vectors of a human face color image and depth image of a target to be recognized and a feature vector of a human face sample image, the present application realizes precise recognition of the human face. Further provided are an electronic apparatus and a computer-readable storage medium.
机译:公开了一种结合深度信息的人脸识别方法。该方法包括:建立人脸图像样本库,其中人脸ID,人脸彩色图像和人脸深度图像彼此匹配;建立并训练人脸分类识别模型,以获得人脸样本图像的特征矢量;获取待识别目标的人脸图像,所述人脸图像包括待识别目标的人脸彩色图像和待识别目标的人脸深度图像;将待识别目标的人脸图像输入人脸分类识别模型,提取待识别目标的人脸图像的特征矢量;根据特征向量,确定与待识别目标的人脸图像对应的人脸ID。结合关于人脸的深度信息,通过计算人脸彩色图像的特征向量与待识别目标的深度图像之间的向量距离与人脸样本图像的特征向量之间的距离精确识别人脸。还提供了电子设备和计算机可读存储介质。

著录项

  • 公开/公告号WO2019071664A1

    专利类型

  • 公开/公告日2019-04-18

    原文格式PDF

  • 申请/专利权人 PING AN TECHNOLOGY (SHENZHEN) CO.LTD.;

    申请/专利号WO2017CN108804

  • 发明设计人 DAI LEI;

    申请日2017-10-31

  • 分类号G06K9;

  • 国家 WO

  • 入库时间 2022-08-21 11:55:16

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