Face Expression Recognition Using Gabor Features and a Novel Weber Local Descriptor

This paper presents a novel fusion approach for facial expression recognition. The novelty of this paper lies in: (i) Gabor wavelets are introduced for image representation, which describes well local spatial scale characteristics and orientation selectivity of image textures. Gabor features are robust to variations due to illumination and noise. Furthermore, we reduce the dimensionality of Gabor feature vector, in order to reduce computation cost and improve discriminative power for feature extraction. (ii) The paper proposes Multi-orientation Symmetric Local Graph Structure (MSLGS) to calculate feature value for replacing differential excitation of Weber Local Descriptor (WLD), which captures more discriminative local images details. The orientation of original WLD also is extended by bringing more gradient direction, thus it can obtain more precise image description to spatial structure information. The comparative experimental results illustrated that the algorithm could achieve a superior performance with high accuracy.