Abstract
For face recognition, conventional dictionary learning (DL) methods have disadvantages. In the paper, we propose a novel robust, discriminative and comprehensive DL (RDCDL) model. The proposed model uses sample diversities of the same face image to make the dictionary robust. The model includes class-specific dictionary atoms and disturbance dictionary atoms, which can well represent the data from different classes. Both the dictionary and the representation coefficients of data on the dictionary introduce discriminative information, which improves effectively the discrimination capability of the dictionary. The proposed RDCDL is extensively evaluated on benchmark face image databases, and it shows superior performance to many state-of-the-art sparse representation and dictionary learning methods for face recognition.
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Acknowledgment
This work is supported by the Projects under Grant no. 2015RC16 and 2015RZY01. This work is partially supported by the National Natural Science Foundation for Young Scientists of China (Grant no. 61402289) and National Science Foundation of Guangdong Province (Grant no. 2014A030313558).
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Lin, G., Yang, M., Shen, L., Xie, W., Zheng, Z. (2016). Sample Diversity, Discriminative and Comprehensive Dictionary Learning for Face Recognition. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_12
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DOI: https://doi.org/10.1007/978-3-319-46654-5_12
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