Abstract
In real-world face recognition systems, traditional face recognition algorithms often fail in the case of insufficient training samples. Recently, the face recognition algorithms of sparse representation have achieved promising results even in the presence of corruption or occlusion. However a large over-complete and elaborately designed discriminant training set is still required to form sparse representation, which seems impractical in the single training image per person problems. In this paper, we extend Sparse Representation Classification (SRC) to the one sample per person problem. We address this problem under variant lighting conditions by introducing relighting methods to generate virtual faces. Our diverse and complete training set can be well composed, which makes SRC more general. Moreover, we verify the recognition under different lighting environments by a cross-database comparison.
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© 2011 Springer-Verlag Berlin Heidelberg
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Hu, D., Song, L., Zhi, C. (2011). Multi-illumination Face Recognition from a Single Training Image per Person with Sparse Representation. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19309-5_52
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DOI: https://doi.org/10.1007/978-3-642-19309-5_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19308-8
Online ISBN: 978-3-642-19309-5
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