Abstract
In this paper, motivated by the recent development of sparse representation (SR) and compressive sensing (CS), in order to address one sample problem, we propose two approaches: shifted images +SRC (SSRC) and reconstructed images +SRC (RSRC). Specifically, we generate the multiple images by shifting the original image or reconstructing the original image via PCA(Principle Component Analysis), and regard new images as training samples, and then apply SRC (Sparse Representation-based Classification) on new training samples set. The experimental results on the two popular face databases (ORL and Yale) demonstrate the feasibility and effectiveness of our proposed methods.
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References
Boufounos, P., Baraniuk, R.: Quantization of sparse representations. In: Proceedings of the 2007 Data Compression Conference (2007)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Transactions Pattern Analysis and Machine Intelligence 31, 210–227 (2009)
Wright, J., et al.: Sparse representation for computer vision and pattern recognition. Proceedings of IEEE (2009)
Huang, K., Aviyente, S.: Sparse representation for signal classification. In: Neural Information Processing Systems (2006)
Qiao, L., Chen, S., Tan, X.: Sparsity preserving projections with applications to face recognition. Pattern Recognition 43, 331–341 (2010)
Donoho, D.: Compressed sensing. IEEE Transactions on Information Theory 52, 1289–1306 (2006)
Candès, E.: Compressive sampling. International Congress of Mathematicians 3, 1433–1452 (2006)
Baraniuk, R.: Compressive sensing. IEEE Signal Processing Magazine 24, 118–121 (2007)
Candes, E.J., Tao, T.: Decoding by Linear Programming. IEEE Transactions on Information Theory 51, 4203–4215 (2005)
Candès, E.J., Tao, T.: Reflections on compressed sensing. IEEE Information Theory Society Newsletter 58, 20–23 (2008)
Liu, J., Chen, S., Zhou, Z., Tan, X.: Single image subspace for face pecognition. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds.) AMFG 2007. LNCS, vol. 4778, pp. 205–219. Springer, Heidelberg (2007)
Wang, J., Plataniotis, K.N., Venetsanopoulos, A.N.: Selecting discriminant eigenfaces for face recognition. Pattern Recognition Letters 26, 1470–1482 (2005)
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Chang, X., Zheng, Z., Duan, X., Xie, C. (2010). Sparse Representation-Based Face Recognition for One Training Image per Person. In: Huang, DS., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Lecture Notes in Computer Science, vol 6215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14922-1_50
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DOI: https://doi.org/10.1007/978-3-642-14922-1_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14921-4
Online ISBN: 978-3-642-14922-1
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