A robust static hand gesture recognition system using geometry based normalizations and Krawtchouk moments

Static hand gesture recognition involves interpretation of hand shapes by a computer. This work addresses three main issues in developing a gesture interpretation system. They are (i) the separation of the hand from the forearm region, (ii) rotation normalization using the geometry of gestures and (iii) user and view independent gesture recognition. The gesture image comprising the hand and the forearm is detected through skin color detection and segmented to obtain a binary silhouette. A novel method based on the anthropometric measures of the hand is proposed for extracting the regions constituting the hand and the forearm. An efficient rotation normalization method that depends on the gesture geometry is devised for aligning the extracted hand. These normalized binary silhouettes are represented using the Krawtchouk moment features and classified using a minimum distance classifier. The Krawtchouk features are found to be robust to viewpoint changes and capable of achieving good recognition for a small number of training samples. Hence, these features exhibit user independence. The developed gesture recognition system is robust to similarity transformations and perspective distortions. It can be well realized for real-time implementation of gesture based applications.