Facial Expression Recognition using joint Multi-Resolution Multi-Area ULBP Representation

In this paper, we propose a robust multi-layer texture representation for facial expressions. Our representation is built up using multi-resolution (MR) uniform local binary pattern (ULBP) features on multi-areas (MA) in facial image. Experiments show that this multi-resolution and multi-area (MRMA) strategy could both greatly improve the discriminative ability of texture representation. Based on the proposed MRMA ULBP representation for facial expression, we propose a MRMA ULBP representation + SVM classifier facial expression recognition system. Experiments based on 21 trained one-against-one SVM classifiers show average recognition accuracy of 92.59% on JAFFE database.