Abstract
In this paper we propose a novel Rearranged Modular 2DPCA (Rm2DPCA) algorithm for face recognition. In the proposed algorithm, the original images are first divided into modular images. Then, the sub-images are rearranged to form a 2D matrix. A covariance matrix is constructed directly using all the arranged matrices, and its eigenvectors are derived for image feature extraction. Experiments compared with other similar approaches show that this method has two advantages: One is its better recognition performance, the other is less computational cost.
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Huxidan, Liu, W., Lu, C. (2012). Face Recognition Based on Rearranged Modular 2DPCA. In: Huang, DS., Gan, Y., Gupta, P., Gromiha, M.M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2011. Lecture Notes in Computer Science(), vol 6839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25944-9_51
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DOI: https://doi.org/10.1007/978-3-642-25944-9_51
Publisher Name: Springer, Berlin, Heidelberg
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