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Real-Time Hand Gesture Recognition Using Complex-Valued Neural Network (CVNN)

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Neural Information Processing (ICONIP 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7062))

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Abstract

Computer vision system is one of the newest approaches for human computer interaction. Recently, the direct use of our hands as natural input devices has shown promising progress. Toward this progress, we introduce a hand gesture recognition system in this study to recognize real time gesture in unconstrained environments. The system consists of three components: real time hand tracking, hand-tree construction, and hand gesture recognition. Our main contribution includes: (1) a simple way to represent the hand gesture after applying thinning algorithm to the image, and (2) using a model of complex-valued neural network (CVNN) for real-valued classification. We have tested our system to 26 different gestures to evaluate the effectiveness of our approach. The results show that the classification ability of single-layered CVNN on unseen data is comparable to the conventional real-valued neural network (RVNN) having one hidden layer. Moreover, convergence of the CVNN is much faster than that of the RVNN in most cases.

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References

  1. Starner, T., Pentland: Real-Time American Sign Language Recognition from Video Using Hidden Markov Models, Technical Report 375, MIT Media Lab, Perceptual Computing Group (1995)

    Google Scholar 

  2. Zhao, M., Quek, F.K.H., Wu, X.: Recursive induction learning in hand gesture recognition. IEEE Trans. Pattern Anal. Mach. Intell. 20, 1174–1185 (1998)

    Article  Google Scholar 

  3. Xu, D.: A Neural Network Approach for Hand Gesture Recognition in Virtual Reality Driving Training System of SPG. In: 8th International Conference on Pattern Recognition, vol. 3 (2006)

    Google Scholar 

  4. Shotton, J., Sharp, T.: Real-Time Human Pose Recognition in Parts from Single Depth Images. Statosuedu 2 (2011), retrieved from http://www.stat.osu.edu/dmsl/BodyPartRecognition.pdf

  5. Amin, M.F., Murase, K.: Single-layered complex-valued neural network for real-valued classification problems. Neurocomputing 72(46), 945–955 (2009)

    Article  Google Scholar 

  6. Intel Corporation, Open Source Computer Vision Library, Reference Manual, Copyright 1999-2001, http://www.developer.intel.com

  7. Sobel, I., Feldman, G.: A 3x3 Isotropic Gradient Operator for Image Processing. Presented at a Talk at the Stanford Artificial Project (1968); unpublished but often cited

    Google Scholar 

  8. Holt, C.M., Stewart, A., Clint, M., Perrott, R.H.: An improved parallel thinning algorithm. Communications of the ACM 30(2), 156–160 (1987)

    Article  Google Scholar 

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© 2011 Springer-Verlag Berlin Heidelberg

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Hafiz, A.R., Amin, M.F., Murase, K. (2011). Real-Time Hand Gesture Recognition Using Complex-Valued Neural Network (CVNN). In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_65

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  • DOI: https://doi.org/10.1007/978-3-642-24955-6_65

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24954-9

  • Online ISBN: 978-3-642-24955-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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