Novel directional patterns and a Generalized Supervised Dimension Reduction System (GSDRS) for facial emotion recognition

This paper presents two novel directional patterns, a Maximum Response-based Directional Texture Pattern (MRDTP) and a Maximum Response-based Directional Number Pattern (MRDNP), for recognizing the facial emotions in constrained as well as unconstrained situations. The intensity information obtained from the maximum of the edge responses, after applying eight Kirsch masks, is used for the calculation of facial features in MRDTP. In MRDNP, instead of intensity information, the direction number of the maximum response is used. After dividing MRDNP and MRDTP code images into grids, feature vectors are created from the concatenated histograms obtained from the grids. This paper also proposes an effective Generalized Supervised Dimension Reduction System (GSDRS) and uses Extreme Learning Machine with Radial Basis Function (ELM-RBF) classifier for rapid and efficient classification of emotions. Both the proposed patterns are more effective than the existing ones in removing random noise and providing good structural information using prominent edges which help to achieve high classification accuracy when tested with seven datasets.