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.
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Wang, Z., Yang, W., Yin, J., Sun, C. (2013). Kernel Collaborative Representation with Regularized Least Square for Face Recognition. In: Sun, Z., Shan, S., Yang, G., Zhou, J., Wang, Y., Yin, Y. (eds) Biometric Recognition. CCBR 2013. Lecture Notes in Computer Science, vol 8232. Springer, Cham. https://doi.org/10.1007/978-3-319-02961-0_16
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DOI: https://doi.org/10.1007/978-3-319-02961-0_16
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