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Recent progress in face recognition based on sparse coding
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Heyan Zhu and Shengping Zhang.
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Yantai University, China : Brown University, USA.
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School of Opto-electronic Information, Yantai University, China : Department of Cognitive, Linguistic & Psychological Sciences, Brown University, USA
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shengping_zhang@brown.edu
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Sparse coding has been attracting increasing interests in computer vision filed, due to its adaptive learning ability and biological inspiration from human vision system. Since sparse representation based classification method for face recognition got great success in 2009, many subsequent improved methods were proposed. In this paper, we aim at providing a comprehensive review of the recent state-of-the-art face recognition methods based on sparse coding. By analyzing their advantages and disadvantages, we summarize the roles of sparse coding in face recognition and discuss the potential improvements in the future.
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