"Corefaces" - robust shift invariant PCA based correlation filter for illumination tolerant face recognition

In this paper we present a novel method for performing robust illumination-tolerant face recognition. We show that this method works well even when presented with partial test faces which are also captured under variable illumination and outperforms other competing face recognition algorithms. Our method is a hybrid PCA-correlation filter which links the best of two major approaches in face recognition; principal component analysis (PCA) for capturing the variability in a set of training images and advanced correlation filters which have attractive features such as illumination tolerance, shift-invariance, and can handle occlusions. We examine how these filters work and why our proposed method is able to perform better. We call our method 'Corefaces' as it seeks to model the 'core' face representation that remains relatively invariant to illumination variations. We show comparative results using the illumination subset of CMU-PIE database consisting of 65 people, and Yale-B illumination database and compare with other standard methods such as the illumination subspace method and Fisherfaces.