• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

A Novel Multiple Kernel Sparse Representation based Classification for Face Recognition

Vol. 8, No.4, April 29, 2014
10.3837/tiis.2014.04.017, Download Paper (Free):

Abstract

It is well known that sparse code is effective for feature extraction of face recognition, especially sparse mode can be learned in the kernel space, and obtain better performance. Some recent algorithms made use of single kernel in the sparse mode, but this didn’t make full use of the kernel information. The key issue is how to select the suitable kernel weights, and combine the selected kernels. In this paper, we propose a novel multiple kernel sparse representation based classification for face recognition (MKSRC), which performs sparse code and dictionary learning in the multiple kernel space. Initially, several possible kernels are combined and the sparse coefficient is computed, then the kernel weights can be obtained by the sparse coefficient. Finally convergence makes the kernel weights optimal. The experiments results show that our algorithm outperforms other state-of-the-art algorithms and demonstrate the promising performance of the proposed algorithms.


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Cite this article

[IEEE Style]
Hao Zheng, Qiaolin Ye and Zhong Jin, "A Novel Multiple Kernel Sparse Representation based Classification for Face Recognition," KSII Transactions on Internet and Information Systems, vol. 8, no. 4, pp. 1463-1480, 2014. DOI: 10.3837/tiis.2014.04.017

[ACM Style]
Zheng, H., Ye, Q., and Jin, Z. 2014. A Novel Multiple Kernel Sparse Representation based Classification for Face Recognition. KSII Transactions on Internet and Information Systems, 8, 4, (2014), 1463-1480. DOI: 10.3837/tiis.2014.04.017