A Personalized Gesture Interaction System with User Identification Using Kinect

In this paper, we present a Kinect-based real time personalized gesture interaction system with user identification targeting for tiled-display environments. By applying a HMM-GSS model and DTW algorithm respectively during user identification and personalized gesture recognition, the system offers more intuitive and user-friendly experience. Our experiment shows that the HMM-GSS model achieves nearly 13.95% accuracy increment than the conventional HMM-based classifier. With feature selection and classifying strategy comparisons, over 95.7% accuracy is obtained by the DTW classifier. Finally, the prototype system can demonstrate a high gesture recognition accuracy in both phases of user identification and real-time interaction with a tiled-display based visualization application.