Understanding gestures with systematic variations in movement dynamics |
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Authors: | Sylvie CW Ong [Author Vitae] [Author Vitae] YV Venkatesh [Author Vitae] |
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Affiliation: | Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576, Singapore |
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Abstract: | Sign language communication includes not only lexical sign gestures but also grammatical processes which represent inflections through systematic variations in sign appearance. We present a new approach to analyse these inflections by modelling the systematic variations as parallel channels of information with independent feature sets. A Bayesian network framework is used to combine the channel outputs and infer both the basic lexical meaning and inflection categories. Experiments using a simulated vocabulary of six basic signs and five different inflections (a total of 20 distinct gestures) obtained from multiple test subjects yielded 85.0% recognition accuracy. We also propose an adaptation scheme to extend a trained system to recognize gestures from a new person by using only a small set of data from the new person. This scheme yielded 88.5% recognition accuracy for the new person while the unadapted system yielded only 52.6% accuracy. |
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Keywords: | Sign language recognition Gesture recognition Bayesian networks Classifier combination Independent channels Signer adaptation |
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