Title :
Principle component analysis and its variants for biometrics
Author :
Chen, Tsuhan ; Hsu, Yufeng Jessie ; Liu, Xiaoming ; Zhang, Wende
Author_Institution :
Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA
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. 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.
Keywords :
biometrics (access control); eigenvalues and eigenfunctions; face recognition; image classification; image registration; principal component analysis; PCA; biometrics; classification scheme; data statistics; distinguishing component analysis; eigenflow; face recognition algorithms; human faces; illumination normalization; illumination variation reduction; multimodal biometric authentication; optimal flow; principle component analysis; registration error; surface reflectance; time-varying statistics; Authentication; Biometrics; Face recognition; Fluid flow measurement; Humans; Lighting; Principal component analysis; Reflectivity; Statistical analysis; Statistics;
Conference_Titel :
Image Processing. 2002. Proceedings. 2002 International Conference on
Print_ISBN :
0-7803-7622-6
DOI :
10.1109/ICIP.2002.1037959