Structural, Syntactic, and Statistical Pattern Recognition

Abstract

Current feature-based gesture recognition systems use human-chosen features to perform recognition. Effective features for classification can also be automatically learned and chosen by the computer. In other recognition domains, such as face recognition, manifold learning methods have been found to be good nonlinear feature extractors. Few manifold learning algorithms, however, have been applied to gesture recognition. Current manifold learning techniques focus only on spatial information, making them undesirable for use in the domain of gesture recognition where stroke timing data can provide helpful insight into the recognition of hand-drawn symbols. In this paper, we develop a new algorithm for multi-stroke gesture recognition, which integrates timing data into a manifold learning algorithm based on a kernel Isomap. Experimental results show it to perform better than traditional human-chosen feature-based systems.