Accurate and robust facial expressions recognition by fusing multiple sparse representation based classifiers

This paper presents an effective and efficient approach based on simulating the information processing procedure of the biological visual system to solve the occlusion problem in facial expression recognition. The proposed method is composed of three components. First, Histograms of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are used to extract features, which imitate the responding to stimuli on visual cortex. Second, Sparse Representation based Classification (SRC) is used due to its robustness to occlusions. Finally, since the recognition results of HOG+SRC and LBP+SRC are complimentary because HOG mainly extracts shape information while LBP primarily represents texture information, a strategy of combining HOG+SRC and LBP+SRC is implemented. Experiments on the Cohn–Kanade database show that the proposed method achieves better performance than many existing methods, and it is robust to both random occlusions and the major component occlusions.