Abstract
Recently, low-rank collaborative representation classification (LCRC) has proven to have good performance under controlled conditions. However, this algorithm stipulates that each singular value of the kernel norm is equal, which limits its ability and flexibility to deal with practical problems. Moreover, training samples and test samples may be damaged due to occlusion or disguise; this factor may reduce the face recognition rate. This paper presents a novel robust face recognition based on discriminative weighted low-rank collaborative representation (WDLCRC). Based on the LCRC, we add the constraint of structural inconsistency and assign the singular values with different weights by adaptively weighting the kernel norm. It is proved through experiments that the recognition rate of WDLCRC on AR database and CMU PIE database is higher than that of SRC, CRC and LCRC algorithms.
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Hou, X., Chen, C., Zhou, S., Li, J. (2018). Discriminative Weighted Low-Rank Collaborative Representation Classifier for Robust Face Recognition. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_28
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DOI: https://doi.org/10.1007/978-3-319-97909-0_28
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