Occlusion-robust bimanual gesture recognition by fusing multi-views

Human hands are dexterous and always be an intuitive way to instruct or communicate with peers. In recent years, hand gesture is widely used as a novel way for human computer interaction as well. However, existing approaches target solely to recognize single-handed gesture, but not gestures with two hands in close proximity (bimanual gesture). Thus, this paper tries to tackle the problems in bimanual gestures recognition which are not well studied from the literature. To overcome the critical issue of hand-hand self-occlusion problem in bimanual gestures, multiple cameras from different view points are used. A tailored multi-camera system is constructed to acquire multi-views bimanual gesture data. By employing both shape and color features, classifiers are trained with our bimanual gestures dataset. A weighted sum fusion scheme is employed to ensemble results predicted from different classifiers. While, the weightings in the fusion are optimized according to how well the recognition performed on a particular view. Our experiments show that multiple-view results outperform single-view results. The proposed method is especially suitable to interactive multimedia applications, such as our two demo programs: a video game and a sign language learner.