On the Selection of a Classification Technique for the Representation and Recognition of Dynamic Gestures

Previous evaluations of gesture recognition techniques have been focused on classification performance, while ignoring other relevant issues such as knowledge description, feature selection, error distribution and learning performance. In this paper, we present an empirical comparison of decision trees, neural networks and hidden Markov models for visual gesture recognition following these criteria. Our results show that none of these techniques is a definitive alternative for all these issues. While neural nets and hidden Markov models show the highest recognition rates, they sacrifice clarity of its knowledge; decision trees, on the other hand, are easy to create and analyze. Moreover, error dispersion is higher with neural nets. This information could be useful to develop a general computational theory of gestures. For the experiments, a database of 9 gestures with more than 7000 samples taken from 15 people was used.

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