A modularly vectorized two dimensional LDA for face recognition

Jvmahong, H.; Liu, Wan-Quan; Lu, C.

doi:10.1109/ICMLC.2012.6359009

Access Status

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Authors

Jvmahong, H.

Liu, Wan-Quan

Lu, C.

Date

2012

Type

Conference Paper

Metadata

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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

Source Conference

International Conference on Machine Learning and Cybernetics (ICMLC 2012)

DOI

10.1109/ICMLC.2012.6359009

ISSN

2160133X

School

Department of Computing

URI

http://hdl.handle.net/20.500.11937/13914

Collection

Curtin Research Publications

Abstract

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.