Visual-based human-machine interface using hand gestures

This paper presents a new paradigm for visual-based interaction with computers using body gestures. The paradigm is based on statistical classification for gesture selection. It has applications in daily interaction with computers, computer games, telemedicine, virtual reality, and sign language studies. Specifically, hand gesture selection and recognition is considered as an example. The aims of this paper are: (a) how to select an appropriate set of gestures having a satisfactory level of discrimination power, and (b) 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. Experimental results confirm better performance of the geometric features compared to moment invariants and Zernike moments.