(untitled) (International Journal of Engineering and Computer Science, 2014)
This paper proposes facial expression recognition in perceptual color space. Tensor perceptual color framework is introduced in this paper for facial expression recognition (FER), which is based on information contained in color facial images. TPCF enables multi linear image analysis in different color space, and demonstrate the color components give the additional information for the robust FER. Using this framework, the components ( in either RGB, CIELab or CIELuv, YCbCr space) of color images are unfolded to 2-D tensors based on multi linear algebra and tensors concepts, from which the feature are extracted by Log-Gabor filters. The mutual information quotients method is employed for the feature selection. Features are classified using multiclass linear discriminate analysis classifiers. Experimental result shows that color information has significant potential to improve emotions recognition performance due to the complementary characteristics of the image textures. The perceptual color spaces (CIELab and CIELuv) are better and overall for FER than color space, by providing more efficient and robust performance for FER using facial images with illumination variation.