Face recognition using data driven local appearance features

DOI

10.2991/icmmcce-15.2015.514How to use a DOI?

Keywords

Face recognition, local appearance features, data driven, feature extraction, point-to-subspace distance.

Abstract

A novel data driven face descriptor based on point-to-subspace metric is proposed for subspace classifiers. Unlike conventional feature descriptors which are carefully designed by hand, the newly proposed method uses supervised learning to derive more robust and more discriminative descriptors. During the feature extraction process, the point-to-subspace distance is used as the inner mechanism to train parameters including filters and weights of different pixels. Experimental results on FERET and Extended Yale B database show that when using subspace classifiers, the proposed feature descriptor is more discriminative and yields higher recognition rate over other features.

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/).

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TY  - CONF
AU  - Xin Xie
AU  - Chao Chen
AU  - Zhijian Chen
PY  - 2015/12
DA  - 2015/12
TI  - Face recognition using data driven local appearance features
BT  - Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmcce-15.2015.514
DO  - 10.2991/icmmcce-15.2015.514
ID  - Xie2015/12
ER  -