Abstract
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.
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Zhang, H., Wu, W., Lou, Y. (2014). A Personalized Gesture Interaction System with User Identification Using Kinect. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_49
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DOI: https://doi.org/10.1007/978-3-319-13560-1_49
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