Real-time hand gesture recognition using complex-valued neural network (CVNN)

Authors:
Abdul Rahman Hafiz;Md. Faijul Amin;Kazuyuki Murase
Affiliations:
Department of Human and Artificial Intelligence System, Graduate School of Engineering, University of Fukui, Japan;Department of Human and Artificial Intelligence System, Graduate School of Engineering, University of Fukui, Japan;Department of Human and Artificial Intelligence System, Graduate School of Engineering, University of Fukui, Japan
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Year:
2011

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Abstract

Computer vision system is one of the newest approaches for human computer interaction. Recently, the direct use of our hands as natural input devices has shown promising progress. Toward this progress, we introduce a hand gesture recognition system in this study to recognize real time gesture in unconstrained environments. The system consists of three components: real time hand tracking, hand-tree construction, and hand gesture recognition. Our main contribution includes: (1) a simple way to represent the hand gesture after applying thinning algorithm to the image, and (2) using a model of complex-valued neural network (CVNN) for real-valued classification. We have tested our system to 26 different gestures to evaluate the effectiveness of our approach. The results show that the classification ability of single-layered CVNN on unseen data is comparable to the conventional real-valued neural network (RVNN) having one hidden layer. Moreover, convergence of the CVNN is much faster than that of the RVNN in most cases.