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

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