计算机应用 ›› 2017, Vol. 37 ›› Issue (3): 801-805.DOI: 10.11772/j.issn.1001-9081.2017.03.801

• 计算机视觉与虚拟现实 • 上一篇    下一篇

视频中人脸位置的定量检测

魏玮, 马瑞, 王小芳   

  1. 河北工业大学 计算机科学与软件学院, 天津 300401
  • 收稿日期:2016-08-15 修回日期:2016-09-25 出版日期:2017-03-10 发布日期:2017-03-22
  • 通讯作者: 马瑞
  • 作者简介:魏玮(1960-),男,山东曲阜人,教授,博士,CCF会员,主要研究方向:机器视觉、模式识别、数据挖掘;马瑞(1992-),女,河北定州人,硕士研究生,主要研究方向:图像处理、模式识别;王小芳(1972-),女,河北邢台人,讲师,博士,主要研究方向:虚拟现实、机器视觉。
  • 基金资助:
    天津市科技计划项目(14RCGFGX00846,15ZCZDNC00130)。

Quantitative detection of face location in videos

WEI Wei, MA Rui, WANG Xiaofang   

  1. School of Computer Science and Engineering, Hebei University of Technology, Tianjin 300401, China
  • Received:2016-08-15 Revised:2016-09-25 Online:2017-03-10 Published:2017-03-22
  • Supported by:
    This work is partially supported by Tianjin Sci-Tech Project (14RCGFGX00846, 15ZCZDNC00130).

摘要: 现有的人脸检测评判标准通常情况下仅仅只是对人脸有无的定性检测,对于视频中人脸位置的定量描述并没有严格的规范;另外,现在的一些研究如视频人脸替换等对视频流中人脸位置的连续性有较高的要求。为了解决上述两个问题,相比之前的人脸检测以及人脸跟踪评估标准,提出了一种视频中人脸位置的定量检测评估标准,并且提出了一种视频中人脸位置的检测方法。该方法首先通过改进的Haar-Like级联分类器在目标区域中检测到人脸初始位置;然后采用金字塔光流法对人脸位置进行预测,同时引入正反向误差检测机制实现对结果的自检测,最终确定人脸位置。实验结果表明,检测标准能够对测试算法在视频人脸检测的定量描述结果给出评判,提出的检测算法在人脸位置的时间一致性上有所提升。

关键词: 视频序列, 人脸检测, 正反向误差, 金字塔光流, 视频闪烁, 时间一致性

Abstract: Available face detection and evaluation standards are usually only a qualitative detection of the face existing, and have no strict norms for the quantitative description of the face location in videos.In addition, some researches such as video face replacement have higher requirements for the continuity of the face position in the video sequences. To solve these two problems, compared with the previous face detection algorithms and the face tracking evaluation standards, a quantitative detection standard of the human face position in the video was proposed, and a modified method of video face position detection was put forward. The initial face location was firstly detected in the target area by the improved Haar-Like cascade classifier; then the pyramid optical flow method was used to predict the position of the face, at the same time the forward-backward error detection mechanism was introduced to the self-checking of results, and finally the location of human face was determined. The experimental results show that the detection standard can give the evaluation of the quantitative description of the detection algorithm in the video face detection, and the proposed detection algorithm has a great improvement in the time consistency of face position in the detection results.

Key words: video sequence, face detection, forward-backward error, pyramid optical flow, video flicker, time consistency

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