Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.patcog.2006.02.010
Title: Understanding gestures with systematic variations in movement dynamics
Authors: Ong, S.C.W. 
Ranganath, S. 
Venkatesh, Y.V. 
Keywords: Bayesian networks
Classifier combination
Gesture recognition
Independent channels
Sign language recognition
Signer adaptation
Issue Date: Sep-2006
Source: Ong, S.C.W., Ranganath, S., Venkatesh, Y.V. (2006-09). Understanding gestures with systematic variations in movement dynamics. Pattern Recognition 39 (9) : 1633-1648. ScholarBank@NUS Repository. https://doi.org/10.1016/j.patcog.2006.02.010
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. © 2006 Pattern Recognition Society.
Source Title: Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/57748
ISSN: 00313203
DOI: 10.1016/j.patcog.2006.02.010
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