Image ratio features for facial expression recognition application

Document Type

Article

Publication Date

6-1-2010

Abstract

Video-based facial expression recognition is a challenging problem in computer vision and humancomputer interaction. To target this problem, texture features have been extracted and widely used, because they can capture image intensity changes raised by skin deformation. However, existing texture features encounter problems with albedo and lighting variations. To solve both problems, we propose a new texture feature called image ratio features. Compared with previously proposed texture features, e.g., high gradient component features, image ratio features are more robust to albedo and lighting variations. In addition, to further improve facial expression recognition accuracy based on image ratio features, we combine image ratio features with facial animation parameters (FAPs), which describe the geometric motions of facial feature points. The performance evaluation is based on the Carnegie Mellon University CohnKanade database, our own database, and the Japanese Female Facial Expression database. Experimental results show that the proposed image ratio feature is more robust to albedo and lighting variations, and the combination of image ratio features and FAPs outperforms each feature alone. In addition, we study asymmetric facial expressions based on our own facial expression database and demonstrate the superior performance of our combined expression recognition system. © 2006 IEEE.

Identifier

77952581437 (Scopus)

Publication Title

IEEE Transactions on Systems Man and Cybernetics Part B Cybernetics

External Full Text Location

https://doi.org/10.1109/TSMCB.2009.2029076

ISSN

10834419

PubMed ID

19884092

First Page

779

Last Page

788

Issue

3

Volume

40

Grant

M58020010

Fund Ref

Nanyang Technological University

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