Abstract
In this paper we propose a machine learning approach to design strong classifiers based on the most relevant combination of 1444 weak classifiers based on pose parameters. This classifier is embedded in a three-layers recognition system which enables us to recognize 70 different gestures performed by various users with high style variability; the recognition ratio is 97.5% with our approach.
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References
Fang, G., Gao, W., Zhao, D.: Large vocabulary sign language recognition based on hierarchical decision trees. In: ICMI 2003, pp. 125–131 (2003)
Munib, Q., Habeeb, M., Takruri, B., et al.: American sign language (ASL) recognition based on Hough transform and neural networks. Expert Systems with Applications 32(1), 24–37 (2007)
Yang, R., Sarkar, S.: Coupled grouping and matching for sign and gesture recognition. Comput. Vis. Image Underst. 113(6), 663–681 (2009)
Liang, X., Li, Q., Zhang, X., Zhang, S., Geng, W.: Performance-driven motion choreographing with accelerometers. Computer Animation and Virtual Worlds 20(2-3), 89–99 (2009)
Viola, P., Jones, M.: Robust Real-Time Face Detection. Int. J. Comput. Vision 57, 137–154 (2004)
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Liang, X., Multon, F., Geng, W. (2012). Machine Learning Approach for Gesture Recognition Based on Automatic Feature Selection. In: Kallmann, M., Bekris, K. (eds) Motion in Games. MIG 2012. Lecture Notes in Computer Science, vol 7660. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34710-8_34
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DOI: https://doi.org/10.1007/978-3-642-34710-8_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34709-2
Online ISBN: 978-3-642-34710-8
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