计算机工程 ›› 2018, Vol. 44 ›› Issue (6): 263-269.doi: 10.19678/j.issn.1000-3428.0049758

• 图形图像处理 • 上一篇    下一篇

基于面部特征点定位的头部姿态估计

闵秋莎,刘能,陈雅婷,王志锋   

  1. 华中师范大学 数字媒体技术系,武汉 430079
  • 收稿日期:2017-12-19 出版日期:2018-06-15 发布日期:2018-06-15
  • 作者简介:闵秋莎(1984—),女,讲师、博士,主研方向为数字图像处理、三维重建、学习分析;刘能、陈雅婷,硕士研究生;王志锋(通信作者),副教授、博士。
  • 基金项目:

    国家自然科学基金(61501199);教育部人文社会科学研究青年基金(17YJC880081)。

Head Pose Estimation Based on Facial Feature Point Localization

MIN Qiusha,LIU Neng,CHEN Yating,WANG Zhifeng   

  1. Department of Digital Media Technology,Central China Normal University,Wuhan 430079,China
  • Received:2017-12-19 Online:2018-06-15 Published:2018-06-15

摘要:

头部姿势估计在许多智能系统中是检测身份和理解行为的关键,但其受光照变化、遮挡、分辨率等因素影响较大。针对彩色二维图像的头部姿态估计方法准确率不高的问题,在分析现有的头部姿态估计方法的基础上,提出一种基于面部特征点定位的头部姿态估计方法。将Adaboost算法和椭圆肤色模型相结合,用于检测人脸,并准确获得图片中的人脸区域。利用Hough圆检测方法定位眼睛和鼻孔,利用人眼和鼻孔的位置信息,将眼睛、鼻子定位结果与正脸头部姿态中的眼睛、鼻子进行对比,从而对不同的头部姿态进行粗估计。实验结果表明,该方法能识别正脸以外的6种不同的头部姿态,总体准确率达到93.53%。

关键词: 人脸检测, 人眼定位, 头部姿态估计, 身份检测, 行为理解

Abstract:

Head pose estimation is the key to detect identity and understand behavior in many intelligent systems,but it is influenced by illumination,occlusion and resolution.Aiming at the problem that the accuracy of head pose estimation for color 2-D images is not high enough,based on the analysis of existing face pose estimation methods,a head pose estimation approach based on facial feature point localization is proposed.This approach combines the Adaboost algorithm and the ellipse skin color model to detect the human face,which provides the accurate face area in the picture.Hough circle detection method is used to locate the eyes and nostrils.By using the position information of the eyes and nostrils,the eye and nose positioning results are compared with the eye nose in the face head posture,so that the different head posture is rough estimated.Experimental results show that this approach can achieve a recognition rate of 93.53% on 6 different head poses face outside.

Key words: face detection, eye location, head pose estimation, identity recognition, behavior understanding

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