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Weighted Sparse Representation Using Collaborative Representation in Kernel Feature Space Based Classification

Kousuke Matsushima 1, Mihoshi Matsusue 2, and Kavin Ruengprateepsang 3
1. Dept. of Control and Information Systems Engineering, National Institute of Technology, Kurume College, Fukuoka, Japan
2. Graduate School of Information Sciences, Tohoku University, Sendai, Japan
3. Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

Abstract—In this paper, we proposed new method to deal non-linear information such as occlusion by using kernel feature space, which named Weighted Sparse Representation using collaborative representation in kernel feature space based classification (WSRCRKC). Kernel Sparse Representation-based Classification (KSRC) has shown good classification performance and robustness for the problem of nonlinear distribution of face images. To use locality information and eliminate the affection of luminance, Weighted Kernel Sparse Representation-based Classification (WKSRC) is proposed as an extension of KSRC by combining multiscale retinex algorithm. Furthermore, by making kernel Gram matrix sparse, we reduce the computation of face recognition. The experimental result shows that our proposal clearly improves the computational time while keeping accuracy high.
Index Terms—face recognition, classification, kernel sparse representation, dimensionality reduction

Cite: Kousuke Matsushima, Mihoshi Matsusue, and Kavin Ruengprateepsang, "Weighted Sparse Representation Using Collaborative Representation in Kernel Feature Space Based Classification," Journal of Advances in Information Technology, Vol. 12, No. 3, pp. 220-225, August 2021. doi: 10.12720/jait.12.3.220-225

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.