Face identification using support vector machines.
Rodrigo Fernández, Emmanuel Viennet · 1999
The Support Vector Machine (SVM) is a statistic learning technique proposed by Vapnik and his research group [8]. In this paper, we benchmark SVMs on a face identification problem and propose two approaches incorporating SV classifiers. The first approach maps the images in to a low dimensional features vector via a local Principal Component Analysis (PCA), features vectors are then used as the inputs of a SVM. The second algorithm is a direct SV classifier with invariances. Both approaches are tested on the freely available ORL database. The SV classifier with invariances achieves an error of 1.5%, which is the best result known on ORL database.