TY - GEN
T1 - Robust modeling and recognition of hand gestures with dynamic Bayesian network
AU - Suk, Heung Il
AU - Sin, Bong Kee
AU - Lee, Seong Whan
PY - 2008
Y1 - 2008
N2 - In this paper, we propose a new gesture recognition model for a set of both one-hand and two-hand gestures based on the dynamic Bayesian network framework which makes it easy to represent the relationship among features and incorporate new information to the model. Unlike the coupled HMM, the proposed model has room for common hidden variables which are believed to be shared between two variables. In an experiment with ten isolated gestures, we obtained a recognition rate upwards of 99.59% with leave-one-out cross validation. The proposed model is believed to have a strong potential for successful applications to other related problems such as sign languages.
AB - In this paper, we propose a new gesture recognition model for a set of both one-hand and two-hand gestures based on the dynamic Bayesian network framework which makes it easy to represent the relationship among features and incorporate new information to the model. Unlike the coupled HMM, the proposed model has room for common hidden variables which are believed to be shared between two variables. In an experiment with ten isolated gestures, we obtained a recognition rate upwards of 99.59% with leave-one-out cross validation. The proposed model is believed to have a strong potential for successful applications to other related problems such as sign languages.
UR - http://www.scopus.com/inward/record.url?scp=77957951328&partnerID=8YFLogxK
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U2 - 10.1109/icpr.2008.4761337
DO - 10.1109/icpr.2008.4761337
M3 - Conference contribution
AN - SCOPUS:77957951328
SN - 9781424421756
T3 - Proceedings - International Conference on Pattern Recognition
BT - 2008 19th International Conference on Pattern Recognition, ICPR 2008
PB - Institute of Electrical and Electronics Engineers Inc.
ER -