Diagonal Fisher linear discriminant analysis for efficient face recognition

Noushath, S. and Hemantha Kumar, G. and Shivakumara, P. (2006) Diagonal Fisher linear discriminant analysis for efficient face recognition. Neurocomputing, 69 (13). 1711 - 1716. ISSN 925-2312

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Official URL: https://doi.org/10.1016/j.neucom.2006.01.012

Abstract

In this paper, a novel subspace method called diagonal Fisher linear discriminant analysis (DiaFLD) is proposed for face recognition. Unlike conventional principal component analysis and FLD, DiaFLD directly seeks the optimal projection vectors from diagonal face images without image-to-vector transformation. The advantage of the DiaFLD method over the standard 2-dimensional FLD (2DFLD) method is, the former seeks optimal projection vectors by interlacing both row and column information of images while the latter seeks the optimal projection vectors by using only row information of images. Our test results show that the DiaFLD method is superior to standard 2DFLD method and some existing well-known methods.

Item Type: Article
Additional Information: Blind Source Separation and Independent Component Analysis
Uncontrolled Keywords: Fisher linear discriminant analysis (FLD), Principal component analysis (PCA), 2-Dimensional FLD, 2-Dimensional PCA, Diagonal FLD, Face recognition, Object recognition
Subjects: D Physical Science > Computer Science
Divisions: Department of > Computer Science
Depositing User: lpa manjunath user
Date Deposited: 17 Aug 2019 09:58
Last Modified: 17 Aug 2019 09:58
URI: http://eprints.uni-mysore.ac.in/id/eprint/6674

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