Abstract
During the past few years, face recognition technique has received significant attention in the fields of computer vision, neuroscience, psychology and others. The robust face recognition casts the problem as an \(\ell _1\)-minimization problem to find a sparse representation of the test image in terms of the training set. The main purpose of this paper is to firstly construct an \(\ell _1\)-\(\ell _1\)-minimization model and secondly be solved via a generalized alternating direction method of multipliers. Most importantly, the model proposed therein contains an adaptive correction term to get sparse representation with higher accuracy. Extensive experiments on the simulated data verify that the proposed method is effective.
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Acknowledgments
The research of Y. Xiao was supported by the National Science Foundation of China (Grant No. 11471101).
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Wu, C., Xiao, Y., Liu, WJ. (2017). Robust Face Recognition Model with Adaptive Correction Term via Generalized Alternating Direction Method of Multipliers. In: Sun, X., Chao, HC., You, X., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2017. Lecture Notes in Computer Science(), vol 10603. Springer, Cham. https://doi.org/10.1007/978-3-319-68542-7_79
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DOI: https://doi.org/10.1007/978-3-319-68542-7_79
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