Hand gesture recognition based on canonical formed superpixel earth mover's distance

This paper presents a new hand gesture recognition algorithm based on canonical formed superpixel earth mover's distance (CF-SP-EMD). SP-EMD is a recently proposed distance metric designed for depth based hand gesture recognition, which shows promising performance. However, in real life, people may have their own habits while performing certain hand gestures. This will yield a variety of hand shapes with different finger poses as compared with the standard templates. Such variety may affect the accuracy of SP-EMD and hence will degrade its performance. In this paper, we propose a new distance metric CF-SP-EMD to alleviate the problem. We organize superpixels in canonical forms that can factor out nonstandard finger poses, resulting a well-structured fingerpose-neutral shape representation for hand gestures. Experimental results using public gesture datasets show that the proposed CF-SP-EMD can achieve better performance for hand gesture recognition, compared with the state-of-art algorithms.