Feature representation for facial expression recognition based on FACS and LBP

被引：14
作者：
Wang L. [1 ]
Li R.-F. [1 ]
Wang K. [1 ]
Chen J. [1 ]
机构：
[1] State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin
来源：
International Journal of Automation and Computing | 2014年 / 11卷 / 05期
基金：
中国国家自然科学基金;
关键词：
active shape models (ASM); facial action coding system (FACS); facial expression recognition; feature representation; Local binary patterns (LBP);
D O I：
10.1007/s11633-014-0835-0
中图分类号：
学科分类号：
摘要：
In expression recognition, feature representation is critical for successful recognition since it contains distinctive information of expressions. In this paper, a new approach for representing facial expression features is proposed with its objective to describe features in an effective and efficient way in order to improve the recognition performance. The method combines the facial action coding system (FACS) and “uniform” local binary patterns (LBP) to represent facial expression features from coarse to fine. The facial feature regions are extracted by active shape models (ASM) based on FACS to obtain the gray-level texture. Then, LBP is used to represent expression features for enhancing the discriminant. A facial expression recognition system is developed based on this feature extraction method by using K nearest neighborhood (K-NN) classifier to recognize facial expressions. Finally, experiments are carried out to evaluate this feature extraction method. The significance of removing the unrelated facial regions and enhancing the discrimination ability of expression features in the recognition process is indicated by the results, in addition to its convenience. © 2014, Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg.
引用
收藏
页码：459 / 468
页数：9
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