Expression recognition based on 2D multi-scale block local Gabor binary patterns

Abstract

In order to accomplish subject-independent facial expression recognition,a facial expression recognition approach based on 2D Multi-scale Block Local Gabor Binary Patterns(2D MB-LGBP)was presented.MB-LGBP features have been proved to be both locally and globally informative for expression recognition.This research combined the idea of MB-LGBP with the concept of Gray Level Co-occurrence Matrix(GLCM)to achieve the 2D MB-LGBP features,which can encode the local textures with structure information.In recognition,SVM classifier was utilized and its performance was compared with the traditional weighted Chi-square distance based paradigm.The experimental result proves the superiority of the 2D MB-LGBP composite features to MB-LGBP and some other popular features in expression recognition.