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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Aharon, M., Elad, M., Bruckstein, A.: K-svd: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing 54(11), 4311–4322 (2006)
Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. IEEE Transactions on PAMI 28(12), 2037–2041 (2006)
Deng, W., Hu, J., Guo, J.: Extended src: Undersampled face recognition via intraclass variant dictionary. IEEE Transactions on PAMI 34(9), 1864–1870 (2012)
Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on PAMI 23(6), 643–660 (2001)
Kim, S., Koh, K., Lustig, M., Boyd, S., Gorinevsky, D.: An interior-point method for large-scale l1-regularized least squares. IEEE Journal of Selected Topics in Signal Processing 1(4), 606–617 (2007)
Lee, D., Seung, H., et al.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)
Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Transactions on Image Processing 11(4), 467–476 (2002)
Yang, M., Zhang, L.: Gabor feature based sparse representation for face recognition with gabor occlusion dictionary. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 448–461. Springer, Heidelberg (2010)
Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. IEEE Transactions on PAMI 31(2), 210–227 (2009)
Yang, M., Zhang, L., Yang, J., Zhang, D.: Robust sparse coding for face recognition. In: Proc. of CVPR, pp. 625–632. IEEE (2011)
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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
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