Face pose estimation with combined 2D and 3D HOG features

Jiaolong Yang*, Wei Liang, Yunde Jia

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

27 引用 (Scopus)

摘要

This paper describes an approach to location and orientation estimation of a person's face with color image and depth data from a Kinect sensor. The combined 2D and 3D histogram of oriented gradients (HOG) features, called RGBD-HOG features, are extracted and used throughout our approach. We present a coarse-to-fine localization paradigm to obtain localization results efficiently using multiple HOG filters trained in support vector machines (SVMs). A feed-forward multi-layer perception (MLP) network is trained for fine face orientation estimation over a continuous range. The experimental result demonstrates the effectiveness of the RGBD-HOG feature and our face pose estimation approach.

源语言英语
主期刊名ICPR 2012 - 21st International Conference on Pattern Recognition
2492-2495
页数4
出版状态已出版 - 2012
活动21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, 日本
期限: 11 11月 201215 11月 2012

出版系列

姓名Proceedings - International Conference on Pattern Recognition
ISSN(印刷版)1051-4651

会议

会议21st International Conference on Pattern Recognition, ICPR 2012
国家/地区日本
Tsukuba
时期11/11/1215/11/12

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引用此

Yang, J., Liang, W., & Jia, Y. (2012). Face pose estimation with combined 2D and 3D HOG features. 在 ICPR 2012 - 21st International Conference on Pattern Recognition (页码 2492-2495). 文章 6460673 (Proceedings - International Conference on Pattern Recognition).