Hand gesture recognition via a new self-organized neural network

Authors:
E. Stergiopoulou;N. Papamarkos;A. Atsalakis
Affiliations:
Image Processing and Multimedia Laboratory, Department of Electrical & Computer Engineering, Democritus University of Thrace, Xanthi, Greece;Image Processing and Multimedia Laboratory, Department of Electrical & Computer Engineering, Democritus University of Thrace, Xanthi, Greece;Image Processing and Multimedia Laboratory, Department of Electrical & Computer Engineering, Democritus University of Thrace, Xanthi, Greece
Venue:
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Year:
2005

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Abstract

A new method for hand gesture recognition is proposed which is based on an innovative Self-Growing and Self-Organized Neural Gas (SGONG) network. Initially, the region of the hand is detected by using a color segmentation technique that depends on a skin-color distribution map. Then, the SGONG network is applied on the segmented hand so as to approach its topology. Based on the output grid of neurons, palm geometric characteristics are obtained which in accordance with powerful finger features allow the identification of the raised fingers. Finally, the hand gesture recognition is accomplished through a probability-based classification method.