Head Pose-Based Conditional Regression Forest for Facial Feature Detection

DOI

10.2991/ameii-15.2015.335How to use a DOI?

Keywords

LPP; regression forests; facial feature point; global feature; head pose.

Abstract

Multi-angles of facial feature detection is still a challenging research. In this paper, the author proposes a precision head pose estimation method as a condition to improve the performance of regression forests, and decreases the missing rate caused by head deflection. The basic idea is used by locality preserving projection, a kind of manifold learning, and nonlinear regression (LPP+NLR) for getting the global information of pose and label it, then utilize trained conditional regression classifier to identify the feature points in global characteristics. The effectiveness of the proposed facial feature detection algorithm is illustrated in the experiments and the comparison with several recent methods.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Liyuan Zhuo
AU  - Huawei Pan
AU  - Chunming Gao
PY  - 2015/04
DA  - 2015/04
TI  - Head Pose-Based Conditional Regression Forest for Facial Feature Detection
BT  - Proceedings of the International Conference on Advances in Mechanical Engineering and Industrial Informatics
PB  - Atlantis Press
SP  - 1805
EP  - 1809
SN  - 2352-5401
UR  - https://doi.org/10.2991/ameii-15.2015.335
DO  - 10.2991/ameii-15.2015.335
ID  - Zhuo2015/04
ER  -