Abstract
A new algorithm is proposed to deal with single training sample face recognition. After geometric normalization of human faces, we generate 13 virtual samples for each face by using geometric transformation and svd decomposition. The distribution of gray value of each image is processed to be normal standard distribution. Finally sparse representation is used to recognize faces. The result of experiments on ORL database shows that our algorithm is better than classical algorithms and new algorithms proposed recently.
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Wang, Z., Zhu, M. (2011). Sparse Representation-Based Face Recognition for Single Example Image per Person. In: Tan, H. (eds) Informatics in Control, Automation and Robotics. Lecture Notes in Electrical Engineering, vol 132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25899-2_61
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DOI: https://doi.org/10.1007/978-3-642-25899-2_61
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