Abstract
Head gestures such as nodding and shaking are often used as one of human body languages for communication with each other, and their recognition plays an important role in the development of Human-Computer Interaction (HCI). As head gesture is the continuous motion on the sequential time series, the key problems of recognition are to track multi-view head and understand the head pose transformation. This paper presents a Bayesian network (BN) based framework, into which multi-view model (MVM) and the head gesture statistic inference model are integrated for recognizing. Finally the decision of head gesture is made by comparing the maximum posterior, the output of BN, with some threshold. Additionally, in order to enhance the robustness of our system, we add the color information into BN in a new way. The experimental results illustrate that the proposed algorithm is effective.
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Lu, P., Huang, X., Zhu, X., Wang, Y. (2005). Head Gesture Recognition Based on Bayesian Network. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3522. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492429_60
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DOI: https://doi.org/10.1007/11492429_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26153-7
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