Facial Expression Recognition by Fusing Gabor and Local Binary Pattern Features

Obtaining effective and discriminative facial appearance descriptors is a challenging task for facial expression recognition (FER). In this paper, a new FER method which combines two of the most successful facial appearance descriptors, namely Gabor filters and Local Binary Patterns (LBPs), is proposed considering that the former one can represent facial shape and appearance over a broader range of scales and orientations while the latter one can capture subtle appearance details. Firstly, feature vectors of Gabor and LBP representations are generated from the preprocessed face images respectively. Secondly, feature fusion is applied to combine these two vectors and dimensionality reduction is conducted. Finally, the Support Vector Machine (SVM) is adopted to classify prototypical facial expressions using still images. The experimental results on the CK+ database demonstrate that the proposed method promotes the performance compared with that using Gabor or LBP descriptor alone, and outperforms several other methods.