Effects of Training Set Dimension on Recognition of Dysmorphic Faces with Statistical Classifiers
Date
2015Author
Saraydemir, Safak
Kayserili, Hulya
Erogul, Osman
TAŞPINAR, NECMİ
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In this paper, an evaluation using various training data sets for discrimination of dysmorphic facial features with distinctive information will be presented. We utilize Gabor Wavelet Transform (GW7) as feature extractor, K-Nearest Neighbor (K-NN) and Support Vector Machines (SVM) as statistical classifiers. We analyzed the classification accuracy according to increasing dimension of training data set, selecting kernel function for SVM and distance metric for K-NN. At the end of the overall classification task, GWT-SVM approach with Radial Basis Function (RBF) kernel type achieved the best classification accuracy rate as 97,5% with 400 images in training data set.
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