期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2014
卷号：7
期号：3
页码：123-134
DOI：10.14257/ijsip.2014.7.3.11
出版社：SERSC
摘要：Head pose can indicate the eye-gaze direction and face toward which is an important part of human motion estimation and understanding. Due to physical factors of the camera, shooting environment, as well as the appearance change of humanity, the head pose estimation becomes a challenging task. Synchronization sub manifold embedding can find the internal structure of nonlinear data for nonlinear dimensionality reduction and random regression forests can make the nonlinear function mapping for getting the right head pose. In this paper, the advantages of these two algorithms are combined with a method for solving the head pose estimation. Data collection step, the depth data come from the 3D sensor; and training data step, the data is using the local linear structure for label and using a statistical model for synchronization pose samples. Meanwhile the experimental results on a publicly available database prove that the proposed algorithm can achieve state-of-the-art performance while the current estimate has a faster speed and higher robustness when large range of pose changes and outperforms existing.
关键词：Synchronized sub manifold embedding; Random regression forests; Head pose ; estimation
