Hand gesture recognition using a dedicated geometric descriptor

A high proportion of hospital-acquired diseases are transmitted nowadays during surgery despite existing asepsis preservation measures. These are quite drastic, prohibiting surgeons from interacting directly with non-sterile equipment. Indirect control is presently achieved through an assistant or a nurse. Gesture-based Human-Computer Interfaces constitute a promising approach for giving direct control over such equipment to surgeons. This paper introduces a novel hand descriptor based on measurements extracted from hand contour convex and concave extrema. Using a 9750-picture database created especially for this purpose, it is compared with three state-of-the-art description methods, namely Hu moments, and both SIFT and HOG features. Effects of large amounts of hand rotation are also studied on each rotation axis independently. Obtained results give HOG features as best in recognizing hands from our database, closely followed by the proposed descriptor. Performance comparison when facing rotated hands shows our descriptor as the most robust to rotations, outperforming the other descriptors by a wide margin.