Abstract
T-CombNET neural network structure has obtained very good results in hand gesture recognition. However one of the most important setting is to define an input space that can optimize the global performance of this structure. In this paper the Interclass Distance Measurement criterion is analyzed and applied to select the space division in T-CombNET structure. The obtained results show that the use of the IDM criterion can improve the classification capability of the network when compared with practical approaches. Simulations using Japanese finger spelling has been done. The recognition rate has improved from 91.2% to 96.5% for dynamic hand motion recognition.
Cite
CITATION STYLE
Lamar, M. V., Bhuiyan, M. S., & Iwata, A. (2000). T-CombNET - A neural network dedicated to hand gesture recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1811, pp. 613–622). Springer Verlag. https://doi.org/10.1007/3-540-45482-9_62
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