HCI Beyond the GUI: Design for Haptic, Speech, Olfactory, and Other Nontraditional Interfaces
HCI Beyond the GUI: Design for Haptic, Speech, Olfactory, and Other Nontraditional Interfaces
IBERAMIA '08 Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence
Learning Relational Grammars from Sequences of Actions
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Hand gesture recognition based on dynamic Bayesian network framework
Pattern Recognition
Proceedings of the 29th ACM international conference on Design of communication
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Naturalness and effectiveness of gesture-based communication strongly depend on the success of gesture recognition. However, confusion in classification increases when considering gestures with similar evolutions. Given that neither typical motion-based features, nor hidden Markov models are capable to distinguish accurately among them, it is common to consider only gestures that require different forms of execution. In this paper, we present empirical evidence showing that, in addition to motion, posture information significantly increases classification rates, even with similar gestures. Moreover, for recognition, we propose dynamic naive Bayesian classifiers. In comparison to hidden Markov models, these models require less iterations of the EM algorithm for training, while keeping competitive classification rates. The proposed system was evaluated considering 9 classes of similar gestures, showing a significant increase in performance by integrating motion and posture attributes.