Confidence-selective moving trace approximation using ellipse and line models for hand gesture recognition

Hand gestures play an important role in human-computer interaction (HCI) system. Areas of applications such as sign language recognition, virtual environment, video games and even surgery require people to interact with devices directly with hands. In this paper, we propose a vision-based system to deal with dynamic hand gesture recognition. We focus on 6 types of gestures: 4 directions for swipe and 2 orientations for rotation. The hand gesture can be identified based on the moving trace of the hand in different video frames. However, different from existing algorithms, the proposed method applies the techniques of ellipse and line approximation to well identify the moving trace and reduce the effect of noise. Moreover, the confidence of each frame is determined by the velocity and the continuity of the motion vector. The frame with less confidence will not be adopted in hand gesture approximation. We also develop a face-based skin region detection to adaptively choose a proper color model. The proposed system is hardware-efficient and can achieve a good accuracy.