Principle component analysis and its variants for biometrics

Abstract

Principle component analysis (PCA) has been widely used for analyzing the statistics of data. While applied to biometrics as a classification scheme, PCA faces certain challenges. In this paper, we present a number of modifications to PCA in order to meet these challenges. Using face recognition as an example, we show how eigenflow, PCA applied to optimal flow, enables us to measure the difference between two images while allowing expression changes and registration error. We show how PCA can be updated to model time-varying statistics. We also show that PCA can be used to model the surface reflectance of human faces and reduce illumination variation that defeats most existing face recognition algorithms. Finally, we present distinguishing component analysis (DCA) and apply it to multimodal biometric authentication.