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|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|
|PDF version:||Click here to retrieve PDF version of paper (79KB)|