Hand Gesture Recognition Algorithm: A Real-Time Human-Body-Based Approach

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Despite the appearance of high-tech human computer interface (HCI) devices, pattern recognition and gesture recognition with single camera are still playing vital role in research. A real-time human-body based algorithm for hand gesture recognition is proposed in this paper. The basis of our approach is a combination of moving object segmentation process and skin color detector based on human body structure to obtain the moving hands from input images, which is able to deal with the problem of complex background and random noises, and a rotate correction process for better finger detection. With ten fingers detected, more than 1000 gestures can be recognized before concerning motion paths. This paper includes experimental results of five gestures, which can be extended to other conditions. Experiments show that the algorithm can achieve a 99 percent recognition average rate and is suitable for real-time applications.

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1338-1343

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February 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] Rafael Bastos and Miguel Sales Dias: Skin Color Profile Capture for Scale and Rotation Invariant Hand Gesture Recognition, edited by Miguel Sales Dias, Gesture-Based Human-Computer Interaction and Simulation, Lecture Notes in Computer Science Springer Berlin / Heidelberg Publisher (2009).

DOI: 10.1007/978-3-540-92865-2_8

Google Scholar

[2] M. Panwar: Hand gesture recognition based on shape parameters, Computing, Communication and Applications (ICCCA), 2012 International Conference on , vol., no., pp.1-6, 22-24 Feb. (2012).

DOI: 10.1109/iccca.2012.6179213

Google Scholar

[3] Haitham Hasan and S. Abdul-Kareem: Static hand gesture recognition using neural networks, Artificial Intelligence Review, DOI: 10. 1007/s10462-011-9303-1, (2012).

DOI: 10.1007/s10462-011-9303-1

Google Scholar

[4] Changick Kim, Jenq-Neng Hwang: Fast and automatic video object segmentation and tracking for content-based applications, Circuits and Systems for Video Technology, IEEE Transactions on , vol. 12, no. 2, pp.122-129, Feb (2002).

DOI: 10.1109/76.988659

Google Scholar

[5] George Bridgman: Bridgman's Complete Guide to Drawing from Life, edited by G. Bridgman, Sterling Publisher (2009).

Google Scholar