Description: Abstract Support Vector Machines are a binary classification method and have demonstrated excellent
results in pattern recognition. Face recognition is a multi-class problem, where the number of classes is
of the known individuals. This paper we use face data extracted from Eigenfeatures and developed a
method to extend SVM to using in multi-class. The training set consists of 5 images of each of the 50
persons equally distributed among frontal, approximately 15°rotated respectively, and the test set
consists of 10 images each of the 50 persons. In the ICT-YC face gallery, the proposed system obtains
competitive results highly: a correct recognition rate of 94.8 for all the 50 persons, to the less number
of the persons and to the famous ORL face gallery we also get good face recognition rate.
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