Face verification advances using spatial dimension reduction methods: 2DPCA & SVM

Authors:
Licesio J. Rodríguez-Aragón;Cristina Conde;Ángel Serrano;Enrique Cabello
Affiliations:
Universidad Rey Juan Carlos, Móstoles, Madrid, Spain;Universidad Rey Juan Carlos, Móstoles, Madrid, Spain;Universidad Rey Juan Carlos, Móstoles, Madrid, Spain;Universidad Rey Juan Carlos, Móstoles, Madrid, Spain
Venue:
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Year:
2005

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Abstract

Spatial dimension reduction called Two Dimensional PCA method has recently been presented. The application of this variation of traditional PCA considers images as 2D matrices instead of 1D vectors as other dimension reduction methods have been using. The application of these advances to verification techniques, using SVM as classification algorithm, is here shown. The simulation has been performed over a complete facial images database called FRAV2D that contains different sets of images to measure the improvements on several difficulties such as rotations, illumination problems, gestures or occlusion. The new method endowed with a classification strategy of SVMs, seriously improves the results achieved by the traditional classification of PCA & SVM.