DEALING WITH VARIABILITY WHEN RECOGNIZING USER'S PERFORMANCE IN NATURAL 3D GESTURE INTERFACES

Abstract

Recognition of natural gestures is a key issue in many applications including videogames and other immersive applications. Whatever is the motion capture device, the key problem is to recognize a motion that could be performed by a range of different users, at an interactive frame rate. Hidden Markov Models (HMM) that are commonly used to recognize the performance of a user however rely on a motion representation that strongly affects the overall recognition rate of the system. In this paper, we propose to use a compact motion representation based on Morphology-Independent features and we evaluate its performance compared to classical representations. When dealing with 15 very similar upper limb motions, HMM based on Morphology-Independent features yield significantly higher recognition rate (84.9%) than classical Cartesian or angular data (70.4% and 55.0%, respectively). Moreover, when the unknown motions are performed by a large number of users who have never contributed to the learning process, the recognition rate of Morphology-Independent input feature only decreases slightly (down to 68.2% for a HMM trained with the motions of only one subject) compared to other features (25.3% for Cartesian features and 17.8% for angular features in the same conditions). The method is illustrated through an interactive demo in which three virtual humans have to interactively recognize and replay the performance of the user. Each virtual human is associated with a HMM recognizer based on the three different input features.