Trajectory-Based Hand Gesture Recognition Using Kinect via Deterministic Learning

The aim of this study is to develop a new trajectory-based method for hand gesture recognition using Kinect via deterministic learning. The recognition approach consists of two stages: a training stage and a recognition stage. In the training stage, trajectory-based hand gesture features are derived from Kinect. Hand motion dynamics underlying motion patterns of different gestures which represent capital English alphabets (A-Z) are locally accurately modeled and approximated by radial basis function (RBF) neural networks. The obtained knowledge of approximated hand motion dynamics is stored in constant RBF networks. In the recognition stage, a bank of dynamical estimators is constructed for all the training patterns. Prior knowledge of hand motion dynamics represented by the constant RBF networks is embedded in the estimators. By comparing the set of estimators with a test gesture pattern to be recognized, a set of recognition errors are generated. Finally, experiments are carried out to demonstrate the recognition performance of the proposed method. By using the 2-fold and 10-fold cross-validation styles, the correct recognition rates are reported to be 93.3% and 94.7%, respectively.