Real-time hand status recognition from RGB-D imagery

One of the most critical limitations of Kinect^TM-based interfaces is the need for persistence in order to interact with virtual objects. Indeed, a user must keep her arm still for a not-so-short span of time while pointing at an object with which she wishes to interact. The most natural way to overcome this limitation and improve interface reactivity is to employ a vision module able to recognize simple hand poses (e.g. open/closed) in order to add a state to the virtual pointer represented by the user hand. In this paper we propose a method to robustly predict the status of the user hand in real-time. We jointly exploit depth and RGB imagery to produce a robust feature for hand representation. Finally, we use temporal filtering to reduce spurious prediction errors. We have also prepared a dataset of more than 30K depth-RGB image pairs of hands that is being made publicly available. The proposed method achieves more than 98% accuracy and is highly responsive.