Support vector machines applied to face recognition

Abstract

Face recognition is a K class problem, where K is the number of known individuals; and support vector machines (SVMs) are a binary classification method.By reformulating the face recognition problem and reinterpreting the output of the SVM classifier, we developed a SVM-based face recognition algorithm.The face recognition problem is formulated as a problem in difference space, which models dissimilarities between two facial images.In difference space we formulate face recognition as a two class problem.The classes are; dissimilarities between faces of the same person, and dissimilarities between faces of different people.By modifying the interpretation of the decision surface generated by SVM, we generated a similarity metric between faces that is learned from examples of differences between faces.The SVM-based algorithm is compared with a principal component analysis (PCA) based algorithm on a difficult set of images from the FERET database.Performance was measured for both verification and identification scenarios.The identification performance for SVM is 77-78% versus 54% for PCA.For verification, the equal error rate is 7% for SVM and 13% for PCA.1