Mr. Navin Goel Profile

Navin Goel

Student at Univ of Nevada/Reno

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Proceedings Article | 28 March 2005 Paper

Face recognition experiments with random projection

Navin Goel, George Bebis, Ara Nefian

Proceedings Volume 5779, (2005) https://doi.org/10.1117/12.605553

KEYWORDS: Principal component analysis, Facial recognition systems, Databases, Expectation maximization algorithms, Distortion, Independent component analysis, Machine learning, Detection and tracking algorithms, Visual process modeling, Analytical research

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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.

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