Vision-Based Hand Pose Estimation Through Similarity Search Using The Earth Mover'S Distance

Vision-based hand pose estimation presents unique challenges, particularly if high-fidelity reconstruction is desired. Searching large databases of synthetic pose candidates for items similar to the input offers an attractive means of attaining this goal. The earth mover's distance is a perceptually meaningful measure of dissimilarity that has shown great promise in content-based image retrieval. It is in general, however, a computationally expensive operation and must be used sparingly. The authors investigate a way of economising on its use while preserving much of its accuracy when applied naively in the context of searching for hand pose candidates in large synthetic databases. In particular, a two-tier search method is proposed which achieves similar accuracy with a speed increase of two orders of magnitude. The system performance is evaluated using real input and the results obtained using the different approaches are compared.