Development of human gender identification prototype using back-propagation neural network / Mohd Amin Abas

This project is to develop gender identification system prototype by using back propagation Neural Network (BPNN). Artificial Neural Network is widely used in classification problem and very usable for developing computer vision system. The system is expected to be able to identify and recognize the genders of human. BPNN is a learning that learns by example (Negnevitsky, 2002). This project has been fully developed by Borland C-H- Builder 6 with assist by other software such as Adobe Photoshop as the im^e editor. The feature that has been used is human face itself with eyebrows has been extract as the information for the input node in the input layer. The performance of the network is 10% error based on 20-test subject.