Hand gesture selection and recognition for visual-based human-machine interface

A new paradigm has been proposed for gesture selection and recognition. The paradigm is based on statistical classification, which has applications in telemedicine, virtual reality, computer games, and sign language studies. The aims of this paper are (1) how to select an appropriate set of gestures having a satisfactory level of discrimination power, and (2) comparison of invariant moments (conventional and Zernike) and geometric properties in recognizing hand gestures. Two-dimensional structures, namely cluster-property and cluster-features matrices, have been employed for gesture selection and to evaluate different gesture characteristics. Moment invariants, Zernike moments, and geometric features are employed for classification and recognition rates are compared. Comparative results confirm better performance of the geometric features.