Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - International Conference on Pattern Recognition |
| Publication status | Published - 2008 Dec 1 |
| Event | 2008 19th International Conference on Pattern Recognition, ICPR 2008 - Tampa, FL, United States Duration: 2008 Dec 8 → 2008 Dec 11 |
Other
| Other | 2008 19th International Conference on Pattern Recognition, ICPR 2008 |
|---|---|
| Country | United States |
| City | Tampa, FL |
| Period | 08/12/8 → 08/12/11 |
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ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
Cite this
Robust modeling and recognition of hand gestures with dynamic Bayesian network. / Suk, Heung-Il; Sin, Bong Kee; Lee, Seong Whan.
Proceedings - International Conference on Pattern Recognition. 2008. 4761337.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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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 - 2008/12/1
Y1 - 2008/12/1
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.
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M3 - Conference contribution
AN - SCOPUS:77957951328
SN - 9781424421756
BT - Proceedings - International Conference on Pattern Recognition
ER -