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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Rodríguez-Aragón, L. J., Conde, C., Serrano, Á., & Cabello, E. (2005). Face verification advances using spatial dimension reduction methods: 2DPCA & SVM. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3617 LNCS, pp. 978–985). https://doi.org/10.1007/11553595_120
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