Gait Analysis for Gender Classification Using CASIA Gait Database

A realistic appearance-based representation to discriminate gender from side view gait sequence is introduced here. This gait representation is based on simple features such as moments extracted from orthogonal view video silhouettes of human walking motion. The silhouette image is divided into two regions. The first region includes from the head to the torso region whereas the second region is from torso to the feet region and for each region features are extracted. Finally we employ different pattern classifiers like KNN (KNearest Neighbor) and SVM (Support Vector Machine) to classify the gender. The division of two regions is based on the centroid of the silhouette image. The experimental results show that SVM classifier gives better results when compared to other classifiers. The classification results are expected to be more reliable than those reported in previous papers. The proposed system is evaluated using side view videos of CASIA dataset B.