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Abstract:
Face recognition is a class problem, where 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.
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