Hand gesture recognition using a neural network shape fitting technique |
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| Authors: | E. Stergiopoulou N. Papamarkos |
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| Affiliation: | 1. Department of IT Engineering, Sookmyung Women’s University, Seoul, Republic of Korea;2. Department of Computer Engineering, SunMoon University, A-san, Republic of Korea;3. School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, India;1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China;2. School of Electronic and Information Engineering, South China University of Technology, China |
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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. |
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