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Proceedings Paper

A novel incremental image reduction principal component analysis and its application for face recognition
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Paper Abstract

In this paper, a fast incremental image reduction principal component analysis approach (IIRPCA) is developed for image representation and recognition. As opposed to traditional appearance based image techniques, IRPCA computes the principal components of a sequence of image samples directly on the 2D image matrix incrementally without estimating the covariance matrix. Therefore, IRPCA overcomes the limitations such as the computational cost and memory requirements to making it suitable for real time applications. The feasibility of the proposed approach was tested on a recently published large database consisting of over 2000 face images. IIRPCA shows superiority in terms of computational time, storage and comparable recognition accuracy (94.0%) when compared to recent techniques such as 2DPCA (92.0%) and 2D RPCA (94.5%).

Paper Details

Date Published: 17 March 2008
PDF: 12 pages
Proc. SPIE 6944, Biometric Technology for Human Identification V, 69440B (17 March 2008); doi: 10.1117/12.778722
Show Author Affiliations
R. M. Mutelo, Newcastle Univ. (United Kingdom)
W. L. Woo, Newcastle Univ. (United Kingdom)
S. S. Dlay, Newcastle Univ. (United Kingdom)


Published in SPIE Proceedings Vol. 6944:
Biometric Technology for Human Identification V
B.V.K. Vijaya Kumar; Salil Prabhakar; Arun A. Ross, Editor(s)

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