Facial-component-based bag of words and PHOG descriptor for facial expression recognition

A novel framework of facial appearance and shape information extraction for facial expression recognition is proposed. For appearance extraction, a facial-component-based bag of words method is presented. We segment face images into 4 component regions, and sub-divide them into 4×4 sub-regions. Dense SIFT (scale-invariant feature transform) features are calculated over the sub-regions and vector quantized into 4×4 sets of codeword distributions. For shape extraction, PHOG (pyramid histogram of orientated gradient) descriptors are computed on the 4 facial component regions to obtain the spatial distribution of edges. Our framework provides holistic characteristics for the local texture and shape features by enhancing the structure-based spatial information, and makes the local descriptors be possible to be used in facial expression recognition for the first time. The recognition rate achieved by the fusion of appearance and shape features at decision level using the Cohn-Kanade database is 96.33%, which outperforms the state of the arts.