[POSTER] An Adaptive Augmented Reality Interface for Hand Based on Probabilistic Approach

Abstract

In this paper we propose an adaptive Augmented Reality interface for hand gestures based on a probabilistic model. The proposed method provides an in-situ interface and the corresponding functionalities by recognizing a context of hand shape and gesture which requires the accurate recognition of static and dynamic hand states. We present an appearance-based hand feature representation that yields robustness against hand shape variations, and a feature extraction method based on the fingertip likelihood from a GMM model. Experimental results show that both context-sensitivity and accurate hand gesture recognition are achieved throughout the quantitative evaluation and its implementation as a three-in-one virtual interface.