Abstract
With recent development of technology, gesture has become a natural way of non-contact human computer interaction. In the literature, to improve user experience of such kind, there exist many works on gesture recognition. However, most works build a universal model for all users, neglecting the fact that different users may have different gesture styles. In this paper, rather than build a universal model for all users, we propose the PerGrab approach by building user-specific model for each user. It is expected that the model can fit users’ gesture well, hence leading to better performance. Specifically, given a universal model provided by manufacturers, PerGrab first records user-specific gesture styles by asking users to make some sample gestures, and then employs a personalization step to adapt universal model for the users. Experiments on applications show that PerGrab achieves good performance.
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Li, T., Li, M. (2013). PerGrab: Adapting Grabbing Gesture Recognition for Personalized Non-contact HCI. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_93
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DOI: https://doi.org/10.1007/978-3-642-42057-3_93
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