Face recognition using sparse representation classifier with Volterra kernels

Hengjian Li; Lianhai Wang; Jiashu Zhang; Zutao Zhang

doi:10.1117/12.2049796

24 December 2013 Face recognition using sparse representation classifier with Volterra kernels

Hengjian Li, Lianhai Wang, Jiashu Zhang, Zutao Zhang

Proceedings Volume 9067, Sixth International Conference on Machine Vision (ICMV 2013); 906709 (2013) https://doi.org/10.1117/12.2049796
Event: Sixth International Conference on Machine Vision (ICMV 13), 2013, London, United Kingdom

Abstract

Sparse representation based classification (SRC) could not well classify the sample belonging to different classes distribute on the same direction. To solve the problem, a Volterra kernel sparse representation based classification (Volterra-SRC) algorithm is proposed in this paper. Firstly, the original face images are divided into non overlapped patches and then mapped into a high dimensional space by utilizing the Volterra kernels. During the training stage, following by the Fisher criteria, the objective function is defined to obtain the optimal Volterra kernels via maximizing inter-class distances and minimizing intra-class distances simultaneously. During the testing stage, a voting procedure is introduced in conjunction with a sparse representation based classification to decide to which class each individual patch belongs. Finally, the aggregate classification results of all patches in a face are used to determine the overall recognition outcome for the given face image. We demonstrate the experiments on ORL and Extended Yale B benchmark face databases and show that our proposed Volterra-SRC algorithm consistently outperforms the original SRC and the proposed has some advantages and robustness in case of small train number samples.

Citation Download Citation

Hengjian Li, Lianhai Wang, Jiashu Zhang, and Zutao Zhang "Face recognition using sparse representation classifier with Volterra kernels", Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 906709 (24 December 2013); https://doi.org/10.1117/12.2049796

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