Abstract
A new method based on sparse representation for robust face recognition is presented in this paper. Inspired by “sparse group lasso,” the individual and group sparsity constraints are integrated into a unified optimization framework. Different with the traditional sparse representation classification (SRC) method, the group sparsity constraint is employed to explore the structure information embedded in the training set. By combining the individual and group sparsity constraints, the sparsity of coefficient within each group can be guaranteed as well. Experimental results on the Extended Yale B database show that the proposed method can achieve state-of-art recognition result, especially in the low-dimension cases.
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Acknowledgments
This paper is supported by the National Natural Science Foundation of China under Grant No. 61201396 and the Research Fund for the Doctoral Program of Higher Education 20113401130001 and Guangdong Joint Foundation Key Project U1201255.
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Chen, T., Qu, L., Wei, S. (2014). Robust Face Recognition with Individual and Group Sparsity Constraints. In: Wen, Z., Li, T. (eds) Foundations of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 277. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54924-3_10
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DOI: https://doi.org/10.1007/978-3-642-54924-3_10
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