Abstract
Hand gestures are one of the main alternatives for Human-Computer Interaction. For this reason, a hand gesture recognition system using near-infrared imagery acquired by a Leap Motion sensor is proposed. The recognition system directly characterizes the hand gesture by computing a global image descriptor, called Depth Spatiograms of Quantized Patterns, without any hand segmentation stage. To deal with the high dimensionality of the image descriptor, a Compressive Sensing framework is applied, obtaining a manageable image feature vector that almost preserves the original information. Finally, the resulting reduced image descriptors are analyzed by a set of Support Vectors Machines to identify the performed gesture independently of the precise hand location in the image. Promising results have been achieved using a new hand-based near-infrared database.
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Acknowledgements
This work has been partially supported by the Ministerio de Economía y Competitividad of the Spanish Government under project TEC2013-48453 (MR-UHDTV), and by AIRBUS Defense and Space under project SAVIER.
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Mantecón, T., del-Blanco, C.R., Jaureguizar, F., García, N. (2016). Hand Gesture Recognition Using Infrared Imagery Provided by Leap Motion Controller. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2016. Lecture Notes in Computer Science(), vol 10016. Springer, Cham. https://doi.org/10.1007/978-3-319-48680-2_5
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