Title: Robust facial expression recognition using improved sparse classifier

Authors: Shiqing Zhang; Gang Zhang; Xiaoming Zhao

Addresses: Institute of Image Processing and Pattern Recognition, Taizhou University, Taizhou 318000, China ' Bay Area Compliance Labs. Corp., Shenzhen 518000, China ' Institute of Image Processing and Pattern Recognition, Taizhou University, Taizhou 318000, China

Abstract: Recently, sparse classifier (SC) has become a promising classification technique and is increasingly attracting attention in signal processing, computer vision and pattern recognition. In this paper, a new classification algorithm based on a weighted sparse representation model, called improved sparse classifier, is proposed for robust facial expression recognition. The effectiveness and robustness of the proposed method is investigated on clean and occluded facial expression images. The performance of the proposed method on robust facial expression recognition is compared with SC, the nearest neighbour (NN), linear support vector machines (SVM) and the nearest subspace (NS). Experimental results on two benchmarking facial expression databases, i.e., the JAFFE database and the Cohn-Kanade database, demonstrate that the proposed method obtains promising performance and a strong robustness to corruption and occlusion on robust facial expression recognition tasks.

Keywords: facial expression recognition; sparse classifiers; sparse representation; image corruption; image occlusion; facial expressions; face recognition; biometrics; nearest neighbour; support vector machines; SVM; nearest subspace.

DOI: 10.1504/IJCAT.2015.071420

International Journal of Computer Applications in Technology, 2015 Vol.52 No.1, pp.59 - 70

Published online: 27 Aug 2015 *

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