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Proceedings Paper

Face recognition experiments with random projection
Author(s): Navin Goel; George Bebis; Ara Nefian
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Paper Abstract

There has been a strong trend lately in face processing research away from geometric models towards appearance models. Appearance-based methods employ dimensionality reduction to represent faces more compactly in a low-dimensional subspace which is found by optimizing certain criteria. The most popular appearance-based method is the method of eigenfaces that uses Principal Component Analysis (PCA) to represent faces in a low-dimensional subspace spanned by the eigenvectors of the covariance matrix of the data corresponding to the largest eigenvalues (i.e., directions of maximum variance). Recently, Random Projection (RP) has emerged as a powerful method for dimensionality reduction. It represents a computationally simple and efficient method that preserves the structure of the data without introducing significant distortion. Despite its simplicity, RP has promising theoretical properties that make it an attractive tool for dimensionality reduction. Our focus in this paper is on investigating the feasibility of RP for face recognition. In this context, we have performed a large number of experiments using three popular face databases and comparisons using PCA. Our experimental results illustrate that although RP represents faces in a random, low-dimensional subspace, its overall performance is comparable to that of PCA while having lower computational requirements and being data independent.

Paper Details

Date Published: 28 March 2005
PDF: 12 pages
Proc. SPIE 5779, Biometric Technology for Human Identification II, (28 March 2005); doi: 10.1117/12.605553
Show Author Affiliations
Navin Goel, Univ. of Nevada/Reno (United States)
George Bebis, Univ. of Nevada/Reno (United States)
Ara Nefian, Intel Corp. (United States)


Published in SPIE Proceedings Vol. 5779:
Biometric Technology for Human Identification II
Anil K. Jain; Nalini K. Ratha, Editor(s)

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