Hand gesture recognition using a neural network shape fitting technique

Authors:
E. Stergiopoulou;N. Papamarkos
Affiliations:
Image Processing and Multimedia Laboratory, Department of Electrical & Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece;Image Processing and Multimedia Laboratory, Department of Electrical & Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
Venue:
Engineering Applications of Artificial Intelligence
Year:
2009

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Abstract

A new method for hand gesture recognition that is based on a hand gesture fitting procedure via a new Self-Growing and Self-Organized Neural Gas (SGONG) network is proposed. Initially, the region of the hand is detected by applying a color segmentation technique based on a skin color filtering procedure in the YCbCr color space. Then, the SGONG network is applied on the hand area so as to approach its shape. Based on the output grid of neurons produced by the neural network, palm morphologic characteristics are extracted. These characteristics, in accordance with powerful finger features, allow the identification of the raised fingers. Finally, the hand gesture recognition is accomplished through a likelihood-based classification technique. The proposed system has been extensively tested with success.