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 aprproach 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 evaulated via SVM (Support Vector Machines) and the classification results were compared.
|Name||2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011|
|Conference||2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011|
|Period||20/04/11 → 22/04/11|