Weighted Local Directional Pattern for Robust Facial Expression Recognition

Abstract

A novel low-cost highly discriminatory feature space is introduced for facial expression recognition, which incorporates a weight to the Local Direction Pattern (LDP), capable of robust performance over a range of image resolutions. In addition, we use Adaboost to pick a small set of high-flying features, which are used by the Support Vector Machine (SVM) to classify facial expressions proficiently. Experimental results show that the proposed technique improves both the accuracy and the speed of the final classifier compares to other existing state-of-the-art methods.