Facial expression recognition research based on blocked local feature

DOI

10.2991/icmcm-16.2016.97How to use a DOI?

Keywords

Gabor features; block Procrustes analysis; PCA; fused features

Abstract

Focus on the key feature extraction and selection of facial expression recognition, this paper firstly extracts the global Gabor features and facial key expression as local features. Then centering, rotating and scaling every feature block with Procrustes analysis to reduce the effect of position and size inconsistent. At last, reduce the dimension of global Gabor features and local features with PCA algorithm and combine them to fused features. Experimental results show that whether the single features or fused features, blockProcrustes can obviously improve the expression recognition performance in most cases, especially increase the stability and maximum of recognizing accuracy.

Copyright

© 2016, 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  - Erdong Zhang
AU  - Shuo Xu
AU  - Peng Zhang
PY  - 2016/12
DA  - 2016/12
TI  - Facial expression recognition research based on blocked local feature
BT  - Proceedings of the 2016 7th International Conference on Mechatronics, Control and Materials (ICMCM 2016)
PB  - Atlantis Press
SP  - 502
EP  - 507
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmcm-16.2016.97
DO  - 10.2991/icmcm-16.2016.97
ID  - Zhang2016/12
ER  -