Local dominant binary patterns for recognition of multi-view facial expressions

In this paper, a novel framework is proposed for automatic recognition of facial expressions. However, the face images for the proposed problem are captured at multiple view angle (i.e., multi-view facial expressions). The proposed scheme introduces a local dominant binary pattern (LDBP). Unlike uniform LBP based features, the LDBP uses fewer feature dimension without affecting the recognition performances. The LDBP is computed by improvising LBP with dominant orientations of neighborhood pixels. The eigen-value analysis of structure tensor representation of expressive face images determines the dominant directions of gray value changes in local neighbors of pixels. We use SVM for view-specific classification of multi-view facial expressions. The proposed model is experimented with the benchmark datasets of both near-frontal (CK+ and JAFEE) and multi-view (KDEF, SFEW and LFPW) face images. The datasets include faces from posed as well as spontaneous expressions. The proposed scheme outperforms state-of-the-arts by approximately 1% for the near-frontal facial expressions and by at least 3% for multi-view facial expressions on an average.