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Enhanced Gesture Recognition Performance through Improved Pre-Processing

by Jacob Grosek, Peizhe Shi, J. Nathan Kutz
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 62 - Number 9
Year of Publication: 2013
Authors: Jacob Grosek, Peizhe Shi, J. Nathan Kutz
10.5120/10105-4757

Jacob Grosek, Peizhe Shi, J. Nathan Kutz . Enhanced Gesture Recognition Performance through Improved Pre-Processing. International Journal of Computer Applications. 62, 9 ( January 2013), 1-8. DOI=10.5120/10105-4757

@article{ 10.5120/10105-4757,
author = { Jacob Grosek, Peizhe Shi, J. Nathan Kutz },
title = { Enhanced Gesture Recognition Performance through Improved Pre-Processing },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 62 },
number = { 9 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume62/number9/10105-4757/ },
doi = { 10.5120/10105-4757 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:11:18.290188+05:30
%A Jacob Grosek
%A Peizhe Shi
%A J. Nathan Kutz
%T Enhanced Gesture Recognition Performance through Improved Pre-Processing
%J International Journal of Computer Applications
%@ 0975-8887
%V 62
%N 9
%P 1-8
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Gesture recognition is analyzed on a set of static hand gestures in the context of designing robust, real-time pre-processing techniques for applications in hand-held electronics. A comparative case study that uses various combinations of algorithms across the steps of the recognition process is made, revealing the fact that many method combinations can produce highly accurate results, even at low resolutions, given the right kind of pre-processing. The pre-processing includes the hand segmentation and normalization done before feature extraction. Indeed, pre-processing has by far the greatest effect on the overall accuracy, robustness, and speed of the gesture recognition process, significantly outweighing the influence of feature-extraction and classification. Even at image resolutions as low as 8 8 pixels, accuracies of 99% are achieved using a simple PCA feature selection scheme and a LDA classification method. These results suggest the priority and advantages of focusing on developing robust and efficient pre-processing methods.

References
  1. Y. Benezeth, P. Jodoin, B. Emile, H. Laurant, and C. Rosenberger. Review and Evaluation of Commonly-Implemented Background Subtraction Algorithms. In IEEE International Conference on Pattern Recognition, pages 1–4, 2008.
  2. Henrik Birk, Thomas B. Moeslund, and Claus B. Madsen. Real-Time Recognition of Hand Alphabet Gestures Using Principal Component Analysis. In In 10th Scandinavian Conference on Image Analysis, 1997.
  3. S. Cobos, M. Ferre, M. A. Sanchez-Uran, J. Ortego, and R. Aracil. Human hand descriptions and gesture recognition for object manipulation. Comp. Meth. Biomechanics Biomedical Eng. , pages 1–13, 2010.
  4. H. Cooper and R. Bowden. Large Lexicon Detection of Sign Language. Human-Computer Interaction, Lecture Notes in Computer Science, 4796:88–97, 2007.
  5. Marcel datasets. (http://www. idiap. ch/resource/gestures/).
  6. P. Dreuw. RWTH German Fingerspelling Database, 2005.
  7. P. Dreuw, T. Deselaers, D. Keysers, and H. Ney. Modeling Image Variability in Appearance-Based Gesture Recognition. In ECCV Workshop on Statistical Methods in Multi- Image and Video Processing, pages 7–18, 2006.
  8. A. Erol, G. Bebis, M. Nicolescu, R. Boyle, and X. Twombly. Vision-based hand pose estimation: A review. Computer Vision and Image Understanding, 108:52–73, 2007.
  9. G. C. Feng and P. C. Yuen. Variance projection function and its application to eye detection for human face recognition. Pattern Recogn. Lett. , 19:899–906, 1998
  10. J. Flusser. On the Independence of Rotation Moment Invariants. Patt. Recog. , 33:1405–1410, 2000.
  11. J. Flusser. Moment Invariants in Image Analysis. World Academy of Science, Eng. & Tech. , 11:376–381, 2005.
  12. Y. Freund and R. Schapire. A Desicion-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55:119– 139, 1997.
  13. Y. Freund and R. Schapire. A Short Introduction to Boosting. J. of Jap. Soc. for Artificial Intelligence, 14:771–780, 1999.
  14. Cambridge hand gesture datasets. (http://www. iis. ee. ic. ac. uk/tkkim/ges db. htm).
  15. S. Haykin. Neural Networks: a comprehensive foundation. Macmillan, New York, 1994.
  16. M. Hu. Visual Pattern Recognition by Moment Invariants. IRE Transactions Information Theory, IT-8:179–187, 1962.
  17. B. Ionescu, D. Coquin, P. Lambert, and V. Buzuloiu. Dynamic Hand Gesture Recognition Using the Skeleton of the Hand. EURASIP J. on App. Signal Processing, 13:2101– 2109, 2005.
  18. T. Khoshgoftaar, E. Allen, L. Bullard, R. Halstead, and G. Trio. A Tree-Based Classification Model for Analysis of a Military Software System. In IEEE Comp. Soc. Proc. of the IEEE High-Assurance Systems Engineering Workshop, pages 244–251, 1997.
  19. J. Lafferty, A. McCallum, and F. Pereira. Conditional Random Fields: Probabilistic Models for Segmentng and Labeling Sequence Data. Proceedings of the International Conference on Machine Learning, pages 282–289, 2001.
  20. J. Lin, Y. Wu, and T. Huang. Modeling the Constraints of Human Hand Motion. In In IEEE Human Motion Workshop, pages 121–126, 2000.
  21. R. Lockton and A. W. Fitzgibbon. Real-time gesture recognition using deterministic boosting. In Proceedings, British Machine Vision Conference, 2002.
  22. S. Menard. Logistic regression: From Introductory to Advanced Concepts and Applications. SAGE Pub. , Inc. , 2010.
  23. S. Mika, G. R¨atsch, J. Weston, B. Sch¨olkopf. , and K. M¨uller. Fisher Discriminant Analysis with Kernels. In Proc. IEEE Workshop Neural Networks for Sig. Proc. , pages 41–48, 1999.
  24. A. Nefian and M. Hayes III. Hidden Markov Models for face identification. In Proceedings of the Int'l Conf. on Acoustics, Speech, and Signal Processing, pages 2721– 2724, 1998.
  25. M. Russell and R. Moore. Explicit Modelling of State Occupancy in Hidden Markov Models for Automatic Speech Recognition. In Proceedings of the Int'l Conf. on Acoustics, Speech, and Signal Processing, pages 5–8, 1985.
  26. F. Samaria and S. Young. HMM-based architecture for face identification. Image and Vision Comp. , 12:537–543, 1994.
  27. P. Soille. Morphological Image Analysis: Principles and Applications. Springer-Verlag, New York, second edition, 2010.
  28. M. Turk and A. Pentland. Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3(1):71–86, 1991.
  29. M. Turk and A. Pentland. Face Recognition Using Eigenfaces. In Proceedings of IEEE Computer Society Conference on Comp. Vision and Patt. Recog. , pages 586–591, 1991.
  30. V. Vapkin. Statistical Learning Theory. Wiley & Sons, 1998.
  31. P. Viola and M. Jones. Robust Real-time Object Detection. In IEEE ICCV Second Int'l Workshop on Stat. and Comp. Theories of Vision, volume 20, pages 1254–1259, 2001.
  32. H. Wu and A. Sutherland. Dynamic Gesture Recognition Using PCA with Multi-scale Theory and HMM. Image Extr. Segm. Recogn. , 4550:132–139, 2001.
  33. X. Zabulis, H. Baltzakis, and A. Argyros. Vision-based hand gesture recognition for human-computer interaction, 2009. The Universal Access Handbook. LEA.
  34. D. Zhang and Z. -H. Zhou. (2D)2PCA: Two-directional two-dimensional PCA for efficient face representation and recognition. Nanocomputing, 69:224–231, 2005.
Index Terms

Computer Science
Information Sciences

Keywords

Pre-processing Hand Gesture Recognition

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