Automatic Hand Gesture Recognition Based on Shape Context

In this paper, we propose a novel method for automatic hand gesture recognition from images based on shape context. Unlike conventional approaches, our method can robustly detect hand gestures rotated with arbitrary angle. Specifically, we improve the existing shape context to rotational invariant by creating a new log-polar space based on the tangent line of the boundary points. We first align the two hand gestures by solving a correspondence problem. The similarity of two hand gestures are obtained by calculating the shape distance based on our proposed rotational invariant shape context. Finally, the best matched result is identified by retrieving the gesture with the maximal shape similarity. Our method is evaluated using a standard simulated gesture dataset. Experimental results show that our method can accurately identify hand gestures, either with or without rotation. Comparison experiments also suggest that our method outperforms existing hand gesture recognition methods based on conventional shape context.