A modularly vectorized two dimensional LDA for face recognition
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Authors
Jvmahong, H.
Liu, Wan-Quan
Lu, C.
Date
2012Type
Conference Paper
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In this paper, a modularly vectorized 2DLDA (Mv2DLDA) is proposed for face recognition. First, the original images are divided into modular blocks. Then, each sub-block is transformed into a vector. By using column vector to represent each modular block, we can obtain a two dimensional matrix representation for image. Finally 2DLDA is applied directly on these 2D matrices. Experimental results on ORL, Yale B and PIE databases show that the proposed method can achieve better recognition performance in comparison with RLDA, 2DPCA and 2DLDA.
Citation
Huxidan and Liu, W. and Lu, C. 2012. A modularly vectorized two dimensional LDA for face recognition, in International Conference on Machine Learning and Cybernetics (ICMLC), Jul 15-17 2012, pp. 695-701. Xian, Shaanxi, China: Institute of Electrical and Electronics Engineers (IEEE).
Source Title
Proceedings of the 2012 International Conference on Machine Learning and Cybernetics
Department
Department of Computing
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