NIST logo

Publication Citation: Support Vector Machines Applied to Face Recognition

NIST Authors in Bold

Author(s): P J. Phillips;
Title: Support Vector Machines Applied to Face Recognition
Published: November 01, 1998
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 re-interpreting 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.
Citation: NIST Interagency/Internal Report (NISTIR) - 6241
Keywords: face recognition;principal component analysis;support vector machines
Research Areas: Biometrics
PDF version: PDF Document Click here to retrieve PDF version of paper (79KB)