Robust Modeling and Recognition of Hand Gestures with Dynamic Bayesian Network

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.