Effects of training set dimension on recognition of dysmorphic faces with statistical classifiers

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 (GWT) 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.