Second IEEE International Conference on Automatic Face and Gesture Recognition (FG '96) Hand sign recognition from intensity image sequences with complex backgrounds Killington, Vermont October 14-October 16 ISBN: 0-8186-7713-9
In this paper, we have presented a new approach to recognize hand signs. In our approach, motion understanding (the hand movement) is tightly coupled with spatial recognition (hand shape). The system uses the multiclass, multidimensional discriminant analysis to automatically select the most discriminating features for gesture classification. A recursive partition tree approximator is proposed to do classification. This approach combined with our previous work on the hand segmentation forms a new framework which addresses three key aspects of the hand sign interpretation, that is the hand shape, the location, and the movement. The framework has been tested to recognize 28 different hand signs. The experimental results show that the system can achieve a 93.1% recognition rate for test sequences that have not been used in the training phase.
Index Terms:
user interfaces; hand sign recognition; intensity image sequences; complex backgrounds; motion understanding; hand movement; spatial recognition; hand shape; multiclass multidimensional discriminant analysis; gesture classification; recursive partition tree approximator; hand segmentation
Citation:
Y. Cui, J.J. Weng, "Hand sign recognition from intensity image sequences with complex backgrounds," fg, pp.259, Second IEEE International Conference on Automatic Face and Gesture Recognition (FG '96), 1996 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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