Abstract
Sparse representation is a significant method to perform image classification for face recognition. Sparsity of the image representation is the key factor for robust image classification. As an improvement to sparse representation-based classification, collaborative representation is a newer method for robust image classification. Training samples of all classes collaboratively contribute together to represent one single test sample. The ways of representing a test sample in sparse representation and collaborative representation are very different, so we propose a novel method to integrate both sparse and collaborative representations to provide improved results for robust face recognition. The method first computes a weighted average of the representation coefficients obtained from two conventional algorithms, and then uses it for classification. Experiments on several benchmark face databases show that our algorithm outperforms both sparse and collaborative representation-based classification algorithms, providing at least a 10% improvement in recognition accuracy.
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Shaoning Zeng received his M.S. degree in software engineering from Beihang University, China, in 2007. Since 2009, he has been a lecturer at Huizhou University, China. His current research interests include pattern recognition, sparse representation, image recognition, and neural networks.
Xiong Yang received his B.S. degree in computer science and technology from Hubei Normal University, China, in 2002. He received his M.S. degree in computer science from Central China Normal University, China, in 2005 and Ph.D. degree from the Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, China, in 2010. Since 2010, he has been teaching in the Department of Computer Science and Technology, Huizhou University, China. His current research interests include pattern recognition and machine learning.
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Zeng, S., Xiong, Y. Weighted average integration of sparse representation and collaborative representation for robust face recognition. Comp. Visual Media 2, 357–365 (2016). https://doi.org/10.1007/s41095-016-0061-5
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DOI: https://doi.org/10.1007/s41095-016-0061-5