Gender Cllassiifiicatiion Usiing Scalled Conjugate Gradiient BackPropagatiion

In this paper, we design an automated system that classifies gender byutilizing a set of human gait data. The gender classification system consists offour stages: in this method, firstly binary silhouette of a walking person isdetected from each frame by using Eigen background method. Secondly, gaitcycle is detected by using aspect ratio method. Thirdly, features from each framein gait cycle are extracted by using: model free method. Finally, neural networkare used for training and testing purposes. The experimental results on CASIA Bdatabase (12 males, 12 females) show that the proposed approach achieves ahigh accuracy in automatic gender classification. Project is designed by Matlab.