Earticle

초록

영어

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.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Head Pose Estimation
  3.1. Synchronization
  3.2. Random Regression Forests
  3.3. Training
 4. Experiments
 5. Conclusions
 References

키워드

저자

• Yulian Zhu [ College of Education, Hebei Normal University of Science & Technology Qinhuangdao, Hebei, P.R.China, 066004 ]
• Zhimei Xue [ College of Education, Hebei Normal University of Science & Technology Qinhuangdao, Hebei, P.R.China, 066004 ]
• Chunyan Li [ College of Education, Hebei Normal University of Science & Technology Qinhuangdao, Hebei, P.R.China, 066004 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605