Multiple-angle hand gesture recognition by fusing SVM classifiers
YT Chen, KT Tseng - 2007 IEEE International Conference on …, 2007 - ieeexplore.ieee.org
YT Chen, KT Tseng
2007 IEEE International Conference on Automation Science and …, 2007•ieeexplore.ieee.orgThis article presents a robust visual system that allows effective recognition of multiple-angle
hand gestures in finger guessing games. Three support vector machine classifiers were
trained for the construction of the hand gesture recognition system. The classified outputs
were fused by proposed plans to improve system performance. Our experimental results
show that the system presented by this article can effectively recognize hand gestures, at
over 93%, of different angles, sizes, and different skin colors.
hand gestures in finger guessing games. Three support vector machine classifiers were
trained for the construction of the hand gesture recognition system. The classified outputs
were fused by proposed plans to improve system performance. Our experimental results
show that the system presented by this article can effectively recognize hand gestures, at
over 93%, of different angles, sizes, and different skin colors.
This article presents a robust visual system that allows effective recognition of multiple-angle hand gestures in finger guessing games. Three support vector machine classifiers were trained for the construction of the hand gesture recognition system. The classified outputs were fused by proposed plans to improve system performance. Our experimental results show that the system presented by this article can effectively recognize hand gestures, at over 93%, of different angles, sizes, and different skin colors.
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