Institute for Pattern Recognition and Artificial Intelligence/ Huazhong University of Science and Technology, Wuhan, China

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Abstract—Recently, the recognition of occluded facial expressions attract more and more people’s attention. Sparse representation based classification (SRC) method gives good performance on face recognition (FR) and facial expression recognition (FER), well-known for its robustness to occlusion. Histograms of Oriented Gradient (HOG) descriptors are very efficient to represent the shape information of different facial expressions and robust to various illumination. Since, this paper proposes a novel method by using HOG descriptors conjunction with SRC framework for FER. Experiment results show that the proposed method gives better performance than the existing state-of-the-art methods. Furthermore, the proposed method is not only robust to assigned occlusions, but also to random occlusions.

Index Terms—Facial expression recognition, local patch, HOG descriptors, Sparse representation based classification (SRC), assigned occlusions, random occlusions.

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Cite: Yan Ouyang, Nong Sang, "Robust Automatic Facial Expression Detection Method," Journal of Software vol. 8, no. 7, pp. 1759-1764, 2013.