Human Identification using Skeletal Gait and Silhouette data extracted by Microsoft Kinect

Since the war on terrorists was declared, human identification area of research has gain its popularity throughout the world. Gait, a biometric information obtained by one's walk, is used to identify a human widely because it can be done unobtrusively. Moreover, it is nearly impossible to alter gait features continuously. In this study, we propose a technique to identify a human using gait data extracted by Microsoft Kinect. We construct a distance function between two walking sequences using combinations of skeletal static features, skeletal kinematic features from movements and silhouette feature (mass vector). The proposed distance function is then used in the classification process along with k-nearest neighbor technique. Our technique yields accuracy of 92.56% which outperforms those techniques proposed by Hong et. al., Cheewakidakarn et. al., Saitong-in et al., Preis et al., Milovanovic et al. and Boulgouris et al. Furthermore, we discover that skeletal kinematic features reveal the unique characteristic of human subjects better than skeletal static and silhouette features.