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. © 2012 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Liang, X., Multon, F., & Geng, W. (2012). Machine learning approach for gesture recognition based on automatic feature selection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7660 LNCS, pp. 366–369). Springer Verlag. https://doi.org/10.1007/978-3-642-34710-8_34
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