Facial emotion classification using concatenated geometric and textural features

Geometric and textural features have been separately used in the literature for facial emotion classification. In this paper, we theoretically and empirically study the capture of facial expressions through geometric and texture-based features, and demonstrate that a simple concatenation of these features can lead to significant improvement in facial emotion classification. We also propose the use of the Directed Acyclic Graph SVM (DAGSVM) for facial emotion classification using the concatenated feature, by analyzing DAGSVM structures. We perform experiments using the well-known extended Cohn-Kanade (CK+), the MUG facial expression (MUG) and the Japanese Female Facial Expression (JAFFE) databases to evaluate the integration of geometric and textural features, and the use of DAGSVM for facial emotion classification. The said integration is found to be effective and DAGSVM is found to be computationally efficient in facial emotion classification.