Video-Based Facial Expression Recognition Using Local Directional Binary Pattern.

Automatic facial expression analysis is a challenging issue and influenced so many areas such as human computer interaction. Due to the uncertainties of the light intensity and light direction, the face gray shades are uneven and the expression recognition rate under simple Local Binary Pattern is not ideal and promising. In this paper we propose two state-of-the-art descriptors for person-independent facial expression recognition. First the face regions of the whole images in a video sequence are modeled with Volume Local Directional Binary pattern (VLDBP), which is an extended version of the LDBP operator, incorporating movement and appearance together. To make the survey computationally simple and easy to expand, only the co-occurrences of the Local Directional Binary Pattern on three orthogonal planes (LDBP-TOP) are debated. After extracting the feature vectors the K-Nearest Neighbor classifier was used to recognize the expressions. The proposed methods are applied to the videos of the Extended Cohn-Kanade database (CK+) and the experimental outcomes demonstrate that the offered techniques achieve more accuracy in comparison with the classic and traditional algorithms.