Background invariant static hand gesture recognition based on Hidden Markov Models

This paper addresses the problem of Static Hand Gesture Recognition (SHGR) and proposes a fast yet simple solution based on Discrete Hidden Markov Models (DHMMs) that use features extracted from the hand contours. In addition to previous work, the use of depth information ensures robustness to the overall system, making it background invariant. Experiments carried on a challenging noisy dataset reveal the superior discriminating as well as generalizing abilities of statistical models, when compared to state-of-the-art methods.