AN INTEGRATED APPROACH TO THE RECOGNITION OF A WIDE CLASS OF CONTINUOUS HAND GESTURES

Abstract

The gesture segmentation is a method that distinguishes meaningful gestures from unintentional movements. Gesture segmentation is a prerequisite stage to continuous gesture recognition which locates the start and end points of a gesture in an input sequence. Yet, this is an extremely difficult task due to both the multitude of possible gesture variations in spatio-temporal space and the co-articulation/movement epenthesis of successive gestures. In this paper, we focus our attention on coping with this problem associated with continuous gesture recognition. This requires gesture spotting that distinguishes meaningful gestures from co-articulation and unintentional movements. In our method, we first segment the input video stream by detecting gesture boundaries at which the hand pauses for a while during gesturing. Next, every segment is checked for movement epenthesis and co-articulation via finite state machine (FSM) matching or by using hand motion information. Thus, movement epenthesis phases are detected and eliminated from the sequence and we are left with a set of isolated gestures. Finally, we apply different recognition schemes to identify each individual gesture in the sequence. Our experimental results show that the proposed scheme is suitable for recognition of continuous gestures having different spatio-temporal behavior.