Comparison of feature extraction and feature selection approaches to decide whether a face image belongs to a male or a female

In this study, a gender recognition system which only uses face images was proposed. Since the dimension of the face images were huge and different from each other; the number of features should be decreased. In order to decrease the dimension of the images Principal Component Analysis (PCA) and a hybrid approach combined by PCA+SFS (Sequential Forward Selection) has been presented and their performances were compared with each other. Via PCA and PCA+SFS hybrid method, the dimension of the dataset was reduced and the proposed system was trained and tested by Support Vector Machine (SVM). The classification results of two dimension reduction approaches according to the extracted features were evaluated via SVM (Support Vector Machines) and the classification results were compared.