Support Vector Features and the role of dimensionality in face authentication

Smeraldi, J. Bigun and W. Gerstner, Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines (SVM 2002), Niagra Falls, Canada, pages 249-259, August 2002

Abstract

A study of the dimensionality of the Face Authentication problem using Principal Component Analysis (PCA) and a novel dimensionality reduction algorithm that we call Support Vector Features (SVFs) is presented. Starting from a Gabor feature space, we show that PCA and SVFs identify distinct subspaces with comparable authentication and generalisation performance. Experiments using KNN classifiers and Support Vector Machines (SVMs) on these reduced feature spaces show that the dimensionality at which saturation of the authentication performance is achieved heavily depends on the choice of the classifier. In particular, SVMs involve directions in feature space that carry little variance and therefore appear to be vulnerable to excessive PCA-based compression.

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