Abstract
Based on the assumption that occlusions have sparse representation on the nature pixel coordinate, Sparse Representation based Classification (SRC) [9] adopts an identity matrix as occlusion dictionary to deal with the occlusions or noises. However, this assumption is often violated in real applications, such as the faces are occluded by scarf. In this paper, we present an approach to learn an occlusion dictionary from the data. Thus, the occlusions have sparse representation on the learned occlusion dictionary and can be effectively separated from the occluded face images. Experimental results show our approach achieves better performance than SRC, while the computational cost is much lower.
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Ou, W., You, X., Zhang, P., Jiang, X., Zhu, Z., Xu, D. (2013). Learning a Sparse Representation for Robust Face Recognition. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_30
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DOI: https://doi.org/10.1007/978-3-642-42051-1_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42050-4
Online ISBN: 978-3-642-42051-1
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