A Hybrid Method for Hand Gesture Recognition

Abstract

Hand gesture recognition aims to recognize the meaningful expressions of hand motion. It is widely used in information visualization, robotics, sign language understanding, medicine and healthcare. Some methods have been proposed for hand gesture recognition. But no single algorithm can handle all kinds of situations, because of the complex environment. In this study, we propose a hybrid method for hand gesture recognition, which extends our previous work on a gesture recognition method based on concept learning by the addition of an association learning process. We use association learning to reveal the frequent patterns in gesture sequences, and then use such patterns to help recognize incomplete gesture sequences. Experiments show the use of association learning does indeed improve recognition accuracy. Experiments also show the hybrid method is comparable to two state of the art methods (HMMs and DTW) for hand gesture recognition, but outperforms them in the larger datasets.