Image ratio features for facial expression recognition application

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
Mingli SongMengchu Zhou

Abstract

Video-based facial expression recognition is a challenging problem in computer vision and human-computer 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 Cohn-Kanade 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 f...Continue Reading

References

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Citations

Dec 30, 2014·IEEE Transactions on Cybernetics·Xiaochun CaoDongdai Lin
Dec 24, 2014·Medical Image Analysis·Erik SmistadFrank Lindseth
Nov 7, 2015·SpringerPlus·Pierluigi CarcagnìCosimo Distante
Aug 17, 2021·Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences·Yingxu WangShushma Patel

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