Model-based gait analysis for gender recognition

Gender classification via model-based human gait data is still immature. Hence in this research, the possibility of side view human gait silhouette to be used as gender recognition is evaluated using model-based approach. Firstly, six attributes located at lower part of human gait specifically from below waist onwards have been identified as the significant points are skeletonized based on the human gait silhouette attained. Next the vertical angles of both hip and knee with respect to thigh for 32 image sequences are extracted as feature vectors followed by feature selection via statistical analysis specifically analysis of variance along with multiple comparison procedure. Further, the resultant of feature selection acted as inputs to the artificial neural network classifier. Initial findings with accuracy of 90% and above confirmed that the proposed method suited to be utilized as gender recognition based on human gait.