CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Survey on Various Gesture Recognition Technologies and Techniques

by Noor Adnan Ibraheem, Rafiqul Zaman Khan
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 50 - Number 7
Year of Publication: 2012
Authors: Noor Adnan Ibraheem, Rafiqul Zaman Khan
10.5120/7786-0883

Noor Adnan Ibraheem, Rafiqul Zaman Khan . Survey on Various Gesture Recognition Technologies and Techniques. International Journal of Computer Applications. 50, 7 ( July 2012), 38-44. DOI=10.5120/7786-0883

@article{ 10.5120/7786-0883,
author = { Noor Adnan Ibraheem, Rafiqul Zaman Khan },
title = { Survey on Various Gesture Recognition Technologies and Techniques },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 50 },
number = { 7 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 38-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume50/number7/7786-0883/ },
doi = { 10.5120/7786-0883 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:47:42.909199+05:30
%A Noor Adnan Ibraheem
%A Rafiqul Zaman Khan
%T Survey on Various Gesture Recognition Technologies and Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 50
%N 7
%P 38-44
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Gestures considered as the most natural expressive way for communications between human and computers in virtual system. Hand gesture is a method of non-verbal communication for human beings for its freer expressions much more other than body parts. Hand gesture recognition has greater importance in designing an efficient human computer interaction system. Using gestures as a natural interface benefits as a motivation for analyzing, modeling, simulation, and recognition of gestures. In this paper a survey on various recent gesture recognition approaches is provided with particular emphasis on hand gestures. A review of static hand posture methods are explained with different tools and algorithms applied on gesture recognition system, including connectionist models, hidden Markov model, and fuzzy clustering. Challenges and future research directions are also highlighted.

References
  1. John Daugman, 1997. Face and Gesture Recognition: Overview, IEEE transaction on pattern analysis and machine intelligence, vol. 19(7).
  2. Sanjay Meena, 2011. A Study on Hand Gesture Recognition Technique, Master thesis, Department of Electronics and Communication Engineering, National Institute of Technology, India.
  3. Myers, B. A. , 1988. A Taxonomy of User Interfaces for Window Managers. IEEE Transaction in Computer Graphics and Applications, 8(5), pp. 65-84. Doi; 10. 1109/38. 7762
  4. Myers B. A. , 1998. A Brief History of Human Computer Interaction Technology, ACM interactions. pp. 44-54, Vol. 5(2). Doi: 10. 1145/274430. 274436
  5. Mokhtar M. Hasan, and Pramod K. Mishra, 2012. Hand Gesture Modeling and Recognition using Geometric Features: A Review, Canadian Journal on Image Processing and Computer Vision Vol. 3, No. 1.
  6. S. Mitra, and T. Acharya, 2007. Gesture Recognition: A Survey. IEEE Transactions on systems, Man and Cybernetics, Part C: Applications and reviews, vol. 37 (3), pp. 311-324, doi: 10. 1109/TSMCC. 2007. 893280.
  7. G. R. S. Murthy & R. S. Jadon, 2009. A Review if Vision Based Hand Gestures Recognition, International Journal of Information Technology and Knowledge Management, vol. 2(2), pp. 405-410.
  8. Thad Starner and Alex Pentland, 1996. Real-Time American Sign Language Recognition from Video Using Hidden Markov Models, AAAI Technical Report FS-96-05, The Media Laboratory Massachusetts Institute of Technology.
  9. C. Keskin, A. Erkan, L. Akarun, 2003. Real Time Hand Tracking and 3D Gesture Recognition for Interactive Interfaces using HMM, In Proceedings of International Conference on Artificial Neural Networks.
  10. Lawrence R. Rabiner, 1989. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proceedings of the IEEE, vol. 77 (2), pp. 257 – 286.
  11. Pengyu H. , Matthew T. , Thomas S. Huang. 2000. Constructing Finite State Machines for Fast Gesture Recognition, IEEE Proceedings, 15th International Conference on Pattern Recognition (ICPR 2000) , vol. 3 ,pp. 3691-694, 2000, doi:10. 1109/ICPR. 2000. 903639.
  12. Xingyan Li, 2003. Gesture Recognition based on Fuzzy C-Means Clustering Algorithm, Department of Computer Science. The University of Tennessee. Knoxville.
  13. David E. Goldberg, 1989. Genetic Algorithms in Search, Optimization, and Machine Learning, Edition 1.
  14. Ben Krose, and Patrick van der Smagtan, 1996. An introduction to Neural Networks, the University of Amsterdam, eighth edition.
  15. sara Bilal, RiniAkmeliawati, Momoh J. El Salami, Amir A. Shafie, 2011. Vision-Based Hand Posture Detection and Recognition for sign Language - A study, IEEE 4th international conference on Mechatronics (ICOM 2011), pp. 1-6.
  16. Vladimir I. Pavlovic, Rajeev Sharma, and Thomas S. Huang, 1997. Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review, IEEE Transactions On Pattern Analysis And Machine Intelligence, vol. 19(7), pp. 677- 695.
  17. PragatiGarg, Naveen Aggarwal and SanjeevSofat, 2009. Vision Based Hand Gesture Recognition, World Academy of Science, Engineering and Technology 49, pp. 972-977.
  18. Thomas B. Moeslund and Erik Granum, 2001. A Survey of Computer Vision-Based Human Motion Capture, Elsevier, Computer Vision and Image Understanding 81, Ideal, pp. 231–268.
  19. Ying Wu, Thomas S. Huang, 1999. Vision-Based Gesture Recognition: A Review, Lecture Notes in Computer Science, Gesture Workshop, proceedings of the international Gesture Workshop on Gesture-Based communication in Human-Computer interaction, vol. (1739), pp. 103-115.
  20. Ali Erol, George Bebis, MirceaNicolescu, Richard D. Boyle, XanderTwombly, 2007. Vision-based hand pose estimation: A review, Elsevier Computer Vision and Image Understanding 108, pp. 52–73.
  21. Laura Dipietro, Angelo M. Sabatini, and Paolo Dario, 2008. Survey of Glove-Based Systems and their applications, IEEE Transactions on systems, Man and Cybernetics, Part C: Applications and reviews, vol. 38(4), pp. 461-482, doi: 10. 1109/TSMCC. 2008. 923862
  22. Joseph J. LaViola Jr. 1999. A Survey of Hand Posture and Gesture Recognition Techniques and Technology, Master Thesis, NSF Science and Technology Center for Computer Graphics and Scientific Visualization, USA.
  23. Luigi Lamberti& Francesco Camastra, 2011. Real-Time Hand Gesture Recognition Using a Color Glove, Springer 16th international conference on Image analysis and processing: Part I (ICIAP'11), pp. 365-373.
  24. Wikipedia websit.
  25. E. Stergiopoulou, N. Papamarkos, 2009. Hand gesture recognition using a neural network shape fitting technique, Elsevier Engineering Applications of Artificial Intelligence 22, pp. 1141–1158.
  26. Tin Hninn H. Maung, 2009. Real-Time Hand Tracking and Gesture Recognition System Using Neural Networks, World Academy of Science, Engineering and Technology 50, pp. 466- 470.
  27. ManarMaraqa, Raed Abu-Zaiter, 2008. Recognition of Arabic Sign Language (ArSL) Using Recurrent Neural Networks, IEEE First International Conference on the Applications of Digital Information and Web Technologies, ICADIWT, Aug. 2008, pp. 478-48, doi: 10. 1109/ICADIWT. 2008. 4664396
  28. Kouichi Murakami and Hitomi Taguchi, 1999. Gesture Recognition using Recurrent Neural Networks, ACM, pp. 237-242.
  29. Shweta K. Yewale, 2011. Artificail Neural Network Approach For Hand Gesture Recognition, International Journal of engineering Science and Technology (IJEST), vol. 3(4).
  30. S. Sidney Fels, Geoffiey E. Hinton, 1993. Glove-Talk: A Neural Network Interface Between a Data-Glove and a Speech Synthesizer, IEEE transaction on Neural Networks, vol. 4(1), pp. 2-8, doi: 10. 1109/72. 182690
  31. S. Sidney Fels, Geoffiey E. Hinton, 1998. Glove-TalkII—A Neural-Network Interface which Maps Gestures to Parallel FormantSpeech Synthesizer Controls, IEEE transactions on neural networks, vol. 9(1), pp. 205-212, doi: 10. 1109/72. 655042
  32. William T. Freeman and Michal Roth, 1995. Orientation Histograms for Hand Gesture Recognition, IEEE International Workshop on Automatic Face and Gesture Recognition, Zurich.
  33. Hanning Zhou, Dennis J. Lin and Thomas S. Huang, 2004. Static Hand Gesture Recognition based on Local Orientation Histogram Feature Distribution Model, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW'04).
  34. Simei G. Wysoski, Marcus V. Lamar, Susumu Kuroyanagi , Akira Iwata, 2002. A rotation invariant approach on static-gesture recognition using boundary histograms and neural networks, IEEE Proceedings of the 9th International Conference on Neural Information Processing, Singapura, November.
  35. James C. Bezdek , Robert Ehrlich, William Full, 1984. FCM: The Fuzzy C-Means Clustering Algorithm, Computers & Geosciences vol. 10(2-3), pp. 191-203.
  36. Damien Zufferey, 2008. Device based gesture recognition, ACM Second International Conference on Tangible and. Embedded Interaction (TEI'08). pp.
  37. Marco Klingmann, 2009. Accelerometer-Based Gesture Recognition with the iPhone, Master Thesis in Cognitive Computing, Goldsmiths University of London.
Index Terms

Computer Science
Information Sciences

Keywords

Hand Posture Hand Gesture Fuzzy Clustering Artificial Neural Network Hidden Markov Model Orientation Histogram

Powered by PhDFocusTM