Abstract
Sparse representation based classification (SRC) has received much attention in computer vision and pattern recognition. SRC is very slow since it needs optimize an objective function with L1-Norm. SRC consists of two parts: collaborative representation and L1-norm constrain. Based on SRC, collaborative representation based classification with regularized least square (CRC-RLS) is prosed. CRC-RLS is a linear method in nature. There are many variations of illumination, expression and gesture in face images. So face recognition is a nonlinear case. Here we propose a kernel collaborative representation based classification with regularized least square (Kernel CRC-RLS, KCRC-RLS) by implicitly mapping the sample into high-dimensional space via kernel tricks. The experimental results on FERET face database demonstrate that Kernel CRC-RLS is effective in classification, leading to promising performance. © Springer International Publishing 2013.
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CITATION STYLE
Wang, Z., Yang, W., Yin, J., & Sun, C. (2013). Kernel collaborative representation with regularized least square for face recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8232 LNCS, pp. 130–137). https://doi.org/10.1007/978-3-319-02961-0_16
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