An Experimental Evaluation of Linear and Kernel-Based Methods for Face Recognition

Authors:
Himaanshu Gupta;Amit K. Agrawal;Tarun Pruthi;Chandra Shekhar;Rama Chellappa
Affiliations:
-;-;-;-;-
Venue:
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Year:
2002

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Abstract

In this paper we present the results of a comparativestudy of linear and kernel-based methods for facerecognition. The methods used for dimensionalityreduction are Principal Component Analysis (PCA),Kernel Principal Component Analysis (KPCA), LinearDiscriminant Analysis (LDA) and Kernel DiscriminantAnalysis (KDA). The methods used for classification areNearest Neighbor (NN) and Support Vector Machine(SVM). In addition, these classification methods areapplied on raw images to gauge the performance of thesedimensionality reduction techniques. All experimentshave been performed on images from UMIST FaceDatabase.