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We describe a method of recognizing hand gestures from hand silhouettes. Given the silhouette of a hand, we compute its convex hull and extract the deficits of convexity corresponding to the differences between the hull and the silhouette. The deficits of convexity are normalized by rotating them around the edges shared with the hull. To learn a gesture, the deficits from a number of examples are extracted and normalized. The deficits are grouped by similarity which is measured by the relative overlap using k-means clustering. Each cluster is assigned a symbol and represented by a template. Gestures are represented by string of symbols corresponding to the nearest neighbors of the deficits. Distinct sequences of symbols corresponding to a given gesture are stored in a dictionary. Given an unknown gesture, its deficits of convexity are extracted and assigned the corresponding sequence of symbols. This sequence is compared with the dictionary of known gestures and assigned to the class to which the best matching string belongs. We used our method to design a gesture interface to control a web browser. We tested our method on five different subjects and achieved a recognition rate of 92% 99%.

USING DEFICITS OF CONVEXITY TO RECOGNIZE HAND GESTURES FROM SILHOUETTES