Abstract
The sparse representation based classifier (SRC) has been successfully applied to robust face recognition (FR) with various disguises. Following SRC, recently regularized robust coding (RRC) was proposed for more robustness to facial occlusion by designing a new robust representation residual term. Although RRC has achieved the leading performance, it ignores the prior knowledge embedded in facial disguises. In this paper, we proposed a novel facial disguise learning (FDL) model, in which the unknown occlusion pattern in the query image is learned using a collected disguise mask dictionary. Two learning strategies with an iterative reweighted coding algorithm, independent FDL and joint FDL, were presented in this paper. The experiments on face recognition with disguise clearly show the advantage of the proposed FDL in accuracy and efficiency.
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Yang, M., Shen, L. (2014). Robust Face Recognition via Facial Disguise Learning. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_33
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DOI: https://doi.org/10.1007/978-3-662-45643-9_33
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