3D hand pose estimation with neural networks

Authors:
Jose Antonio Serra;José Garcia-Rodriguez;Sergio Orts-Escolano;Juan Manuel Garcia-Chamizo;Anastassia Angelopoulou;Alexandra Psarrou;Markos Mentzelopoulos;Javier Montoyo-Bojo;Enrique Domínguez
Affiliations:
Dept. of Computing Technology, University of Alicante, Alicante, Spain;Dept. of Computing Technology, University of Alicante, Alicante, Spain;Dept. of Computing Technology, University of Alicante, Alicante, Spain;Dept. of Computing Technology, University of Alicante, Alicante, Spain;Dept. of Computer Science & Software Engineering (CSSE), University of Westminster, Cavendish, United Kingdom;Dept. of Computer Science & Software Engineering (CSSE), University of Westminster, Cavendish, United Kingdom;Dept. of Computer Science & Software Engineering (CSSE), University of Westminster, Cavendish, United Kingdom;Dept. of Computing Technology, University of Alicante, Alicante, Spain;Dept. of Computer Science, University of Malaga, Spain
Venue:
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
Year:
2013

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Abstract

We propose the design of a real-time system to recognize and interprethand gestures. The acquisition devices are low cost 3D sensors. 3D hand pose will be segmented, characterized and track using growing neural gas (GNG) structure.The capacity of the system to obtain information with a high degree of freedom allows the encoding of many gestures and a very accurate motion capture. The use of hand pose models combined with motion information provide with GNG permits to deal with the problem of the hand motion representation. A natural interface applied to a virtual mirrorwriting system and to a system to estimate hand pose will be designed to demonstrate the validity of the system.