Understanding gestures with systematic variations in movement dynamics

Authors:
Sylvie C. W. Ong;Surendra Ranganath;Y. V. Venkatesh
Affiliations:
Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576, Singapore;Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576, Singapore;Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576, Singapore
Venue:
Pattern Recognition
Year:
2006

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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.