Intra-personal kernel space for face recogni

Authors:
Shaohua Kevin Zhou;Rama Chellappa;Baback Moghaddam
Affiliations:
Center for Automation Research, University of Maryland, College Park, MD;Center for Automation Research, University of Maryland, College Park, MD;Mitsubishi Electric Research Laboratories, Cambridge, MA
Venue:
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Year:
2004

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Abstract

Intra-personal space modeling proposed by Moghaddam et. al. has been successfully applied in face recognition. In their work the regular principal subspaces are derived from the intra-personal space using a principal component analysis and embedded in a probabilistic formulation. In this paper, we derive the principal subspace from the intra-personal kernel space by developing a probabilistic analysis of kernel principal components for face recognition. We test this algorithm on a subset of the FERET database with illumination and facial expression variations. The recognition performance demonstrates its advantage over other traditional subspace approaches.