Abstract
Recognizing a face with significant lighting, disguise and occlusion variations is an interesting and challenging problem in pattern recognition. To address this problem, many regression based methods, represented by sparse representation classifier (SRC), are presented recently. SRC uses the \(L_1\)-norm to characterize the pixel-level sparse noise but ignore the spatial information of noise. In this paper, we find that nuclear-norm is good for characterizing image-wise structural noise, and thus we use the nuclear norm and \(L_1\)-norm to jointly characterize the error image in regression model. Our experimental results demonstrate that the proposed method is more effective than state-of-the-art regression methods for face reconstruction and recognition.
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Luo, L., Yang, J., Qian, J., Tai, Y. (2015). Nuclear-\(L_1\) Norm Joint Regression for Face Reconstruction and Recognition. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9004. Springer, Cham. https://doi.org/10.1007/978-3-319-16808-1_44
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DOI: https://doi.org/10.1007/978-3-319-16808-1_44
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