E-2DLDA: a new matrix-based image representation method for face recognition

Authors:
Long Fei;Dong Huailin;Fan Ling;Chen Haishan
Affiliations:
Software School, Xiamen University, Xiamen, China;Software School, Xiamen University, Xiamen, China;Tan Kah Kee College, Xiamen University, Zhangzhou, China;Software School, Xiamen University, Xiamen, China
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

Citing 6
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Abstract

Two-dimensional linear discriminant analysis (2DLDA) was recently developed for face image representation and recognition by adopting the idea of image projection in 2DPCA. 2DLDA outperforms traditional LDA mainly in terms of feature extraction speed. Unfortunately, 2DLDA needs to use large numbers of features to represent an image sample, causing storage requirements are heavy and also feature matching process is time-consuming. Against this problem, we discuss in this paper a new image representation scheme called Enhanced 2DLDA (E-2DLDA) for face recognition. The main strategy adopted in our method is that two image projections are applied to an image sample jointly, so the dimensions of extracted feature matrix along both horizontal direction and vertical direction get compressed, and finally the total number of features can be reduced to a great extent. The experimental results on ORL database show that this method remarkably outperforms existing 2DLDA in terms of speed of feature matching and storage requirements of features.