Facial Expression Recognition Based on Local Directional Pattern Using SVM Decision-level Fusion

This paper presents a novel expression recognition method based on global and local features with decision-level fusion. We first extract Local Directional Pattern (LDP) global features of the whole face which can guarantee basic expression difference and decrease the influence of no-facial region meanwhile, then the Local Directional Pattern Variance (LDPv) descriptor is used to extract local features of regions of eyes and mouth to extrude their contribution on expression changes. After feature extraction, we don't use feature fusion with simple concatenation, a decision-level fusion for global LDP feature and local LDPv feature by Support Vector Machine (SVM) is selected to recognition respectively. Furthermore, we also research the optimal parameters for regions-dividing and weight of LDPv. Extensive results from two standard databases indicate the effectiveness of our proposed method.