@inproceedings{4817f2d08c394c538f27dd890fe45242,
title = "Kernel collaborative representation with regularized least square for face recognition",
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.",
keywords = "Collaborative representation, Face recognition, Kernel",
author = "Zhenyu Wang and Wankou Yang and Jun Yin and Changyin Sun",
year = "2013",
doi = "10.1007/978-3-319-02961-0\_16",
language = "英语",
isbn = "9783319029603",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "130--137",
booktitle = "Biometric Recognition - 8th Chinese Conference, CCBR 2013, Proceedings",
note = "2012 International Conference on Service-Oriented Computing, ICSOC 2012 ; Conference date: 16-11-2013 Through 17-11-2013",
}