A study on static hand gesture recognition using moments

Hand gesture recognition is one of the key techniques in developing user-friendly interfaces for human-computer interaction. Static hand gestures are the most essential facets of gesture recognition. View point invariance and user independence are among the important requirements for realizing a real time gesture recognition system. In this context, the geometric moments and the orthogonal moments namely the Zernike, Tchebichef and Krawtchouk moments are explored. The proposed system detects the hand region through skin color identification and obtains the binary silhouette. These images are normalized for rotation and scale changes. The moment features of the normalized hand gestures are classified using a minimum distance classifier. The classification results suggest that the Krawtchouk moment features are comparatively robust to view point changes and also exhibit user independence.