FACIAL EXPRESSION RECOGNITION BASED ON IMPROVED LBP OPERATOR AND K-MEANS CLUSTERING

DOI

10.2991/icitmi-15.2015.139How to use a DOI?

Keywords

Feature Extraction, Local Binary Patterns Operator, K-means Clustering, Facial Expression Recognition.

Abstract

For facial expression features extraction and automatic clustering, a method is proposed. This method integrates improved LBP operator and K-means clustering. Firstly, facial features are located by integral and variance projection. Improved LBP operator with changed threshold is figured out. Secondly, K-means clustering produces expression templates. Then K nearest neighbor algorithm recognizes facial expression. Meanwhile, the expression templates will be updated by the result itself, optimizing expression templates dynamically. The algorithm is simple and low computational-complexity. It updates the train results dynamically. Results show that this classification method is accurate.

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  - Yunfei Wang
AU  - Hui Ding
AU  - Yi Liu
AU  - Yanyan Pan
PY  - 2015/10
DA  - 2015/10
TI  - FACIAL EXPRESSION RECOGNITION BASED ON IMPROVED LBP OPERATOR AND K-MEANS CLUSTERING
BT  - Proceedings of the 4th International Conference on Information Technology and Management Innovation
PB  - Atlantis Press
SP  - 829
EP  - 833
SN  - 2352-538X
UR  - https://doi.org/10.2991/icitmi-15.2015.139
DO  - 10.2991/icitmi-15.2015.139
ID  - Wang2015/10
ER  -