INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY, cilt.12, sa.2, ss.205-211, 2015 (SCI-Expanded)
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.