A comparative study of two state-of-the-art sequence processing techniques for hand gesture recognition

In this paper, we address the problem of the recognition of isolated, complex, dynamic hand gestures. The goal of this paper is to provide an empirical comparison of two state-of-the-art techniques for temporal event modeling combined with specific features on two different databases. The models proposed are the Hidden Markov Model (HMM) and Input/Output Hidden Markov Model (IOHMM), implemented within the framework of an open source machine learning library (www.torch.ch). There are very few hand gesture databases available to the research community; consequently, most of the algorithms and features proposed for hand gesture recognition are not evaluated on common data. We thus propose to use two publicly available databases for our comparison of hand gesture recognition techniques. The first database contains both one- and two-handed gestures, and the second only two-handed gestures.