CFP last date
20 May 2024
Reseach Article

A Survey on Vision-based Dynamic Gesture Recognition

by Sumpi Saikia, Sarat Saharia
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 138 - Number 1
Year of Publication: 2016
Authors: Sumpi Saikia, Sarat Saharia

Sumpi Saikia, Sarat Saharia . A Survey on Vision-based Dynamic Gesture Recognition. International Journal of Computer Applications. 138, 1 ( March 2016), 19-27. DOI=10.5120/ijca2016908655

@article{ 10.5120/ijca2016908655,
author = { Sumpi Saikia, Sarat Saharia },
title = { A Survey on Vision-based Dynamic Gesture Recognition },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 138 },
number = { 1 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 19-27 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016908655 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:38:31.385675+05:30
%A Sumpi Saikia
%A Sarat Saharia
%T A Survey on Vision-based Dynamic Gesture Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 138
%N 1
%P 19-27
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

Gesture is the most primitive way of communication among human being. Today in the era of modern technology gesture recognition influences the world very diversely, from the physically challenged people to robot control to virtual reality environments. Compared to the systems which use extra devices (gloves, sensors), vision-based systems are more user-friendly and simple. Vision-based systems are easy to use, but most difficult to implement. This paper presents a comprehensive survey on the vision-based dynamic gesture recognition approaches, a comparative study on those methods, and find out the issues and challenges in this area.

  1. Mitra, S. and Acharya, T. 2007. Gesture recognition: A survey. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 37(3), 311-324.
  2. Englmeier, K-H., et al. "Virtual reality and multimedia human-computer interaction in medicine." Multimedia Signal Processing, 1998 IEEE Second Workshop on. IEEE, 1998.
  3. Thakkar, Varun, et al. "Learning Math Using Gesture." Education and e-Learning Innovations (ICEELI), 2012 International Conference on. IEEE, 2012.
  4. Takano, Kosuke, and Kin Fun Li. "Classifying sports gesture using event-based matching in a multimedia e-learning system." Advanced Information Networking and Applications Workshops (WAINA), 2012 26th International Conference on. IEEE, 2012.
  5. Nussipbekov, A. K., E. N. Amirgaliyev, and Minsoo Hahn. "Kazakh Traditional Dance Gesture Recognition." Journal of Physics: Conference Series. Vol. 495. No. 1. IOP Publishing, 2014.
  6. James, Jodi, et al. "Movement-based interactive dance performance."Proceedings of the 14th annual ACM international conference on Multimedia. ACM, 2006.
  7. Thomas, M. C., and Pradeepa, A. P. M. S. 2014. A COMPREHENSIVE REVIEW ON VISION BASED HAND GESTURE RECOGNITION TECHNOLOGY. International Journal, 2(1).
  8. Pavlovic, V., Sharma, R., and Huang, T. S. 1997. Visual interpretation of hand gestures for human-computer interaction: A review. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 19(7), 677-695.
  9. Li, X. 2003. Gesture recognition based on fuzzy C-Means clustering algorithm.Department Of Computer Science The University Of Tennessee Knoxville.
  10. Quek, F. 1994. Toward a vision-based hand gesture interface. InVirtual Reality Software and Technology Conference (pp. 17-29).
  11. Moeslund, T. B., and Granum, E. 2001. A survey of computer vision-based human motion capture. Computer vision and image understanding, 81(3), 231-268.
  12. Baudel, T., and Beaudouin-Lafon, M. 1993. Charade: remote control of objects using free-hand gestures. Communications of the ACM, 36(7), 28-35.
  13. Fels, S. S., and Hinton, G. E. 1993. Glove-talk: A neural network interface between a data-glove and a speech synthesizer. Neural Networks, IEEE Transactions on, 4(1), 2-8.
  14. Sturman, D. J., and Zeltzer, D. 1994. A survey of glove-based input. Computer Graphics and Applications, IEEE, 14(1), 30-39.
  15. Quam, D. L. 1990. Gesture recognition with a dataglove. In Aerospace and Electronics Conference, 1990. NAECON 1990., Proceedings of the IEEE 1990 National (pp. 755-760). IEEE.
  16. Meena, S. 2011. A Study on Hand Gesture Recognition (Doctoral dissertation, National Institute of Technology, Rourkela).
  17. Stergiopoulou, E., and Papamarkos, N. 2009. Hand gesture recognition using a neural network shape fitting technique. Engineering Applications of Artificial Intelligence, 22(8), 1141-1158.
  18. Chaudhary, A., Raheja, J. L., and Raheja, S. 2012. A vision based geometrical method to find fingers positions in real time hand gesture recognition. Journal of Software, 7(4), 861-869.
  19. Wang, X., et al. "Hidden-markov-models-based dynamic hand gesture recognition." Mathematical Problems in Engineering 2012 (2012).
  20. Garg, P., Aggarwal, N., and Sofat, S. 2009. Vision based hand gesture recognition. World Academy of Science, Engineering and Technology, 49(1), 972-977.
  21. Pan, Z, et al. "A real-time multi-cue hand tracking algorithm based on computer vision." Virtual Reality Conference (VR), 2010 IEEE.
  22. Hackenberg, G., McCall, R., and Broll, W. 2011. Lightweight palm and finger tracking for real-time 3D gesture control. In Virtual Reality Conference (VR), 2011 IEEE (pp. 19-26). IEEE.
  23. Murthy, G. R. S., and Jadon, R. S. 2009. A review of vision based hand gestures recognition. International Journal of Information Technology and Knowledge Management, 2(2), 405-410.
  24. Ghobadi, S.E, et al. "Real time hand based robot control using multimodal images." IAENG International Journal of Computer Science 35.4 (2008): 500-505.
  25. Malima, A., Özgür, E., and Çetin, M. 2006. A fast algorithm for vision-based hand gesture recognition for robot control. In Signal Processing and Communications Applications, 2006 IEEE 14th (pp. 1-4). IEEE.
  26. Eickeler, S., Kosmala, A., and Rigoll, G. 1998. Hidden markov model based continuous online gesture recognition. In Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on (Vol. 2, pp. 1206-1208). IEEE.
  27. Ramamoorthy, A., et al. "Recognition of dynamic hand gestures." Pattern Recognition 36.9 (2003): 2069-2081.
  28. Ionescu, B., et al. "Dynamic hand gesture recognition using the skeleton of the hand." EURASIP Journal on Applied Signal Processing 2005 (2005): 2101-2109.
  29. Verma, R., and Dev, A. 2009. Vision based hand gesture recognition using finite state machines and fuzzy logic. In Ultra Modern Telecommunications & Workshops, 2009. ICUMT'09. International Conference on (pp. 1-6). IEEE.
  30. Zou, Z, et al. "Dynamic hand gesture recognition system using moment invariants." Information and Automation for Sustainability (ICIAFs), 2010 5th International Conference on. IEEE, 2010.
  31. Suk, H. I., Sin, B. K., and Lee, S. W. 2010. Hand gesture recognition based on dynamic Bayesian network framework. Pattern Recognition, 43(9), 3059-3072.
  32. Yu, S., et al. "Vision-based continuous sign language recognition using product HMM." Pattern Recognition (ACPR), 2011 First Asian Conference on. IEEE, 2011.
  33. Yang, Z., et al. "Dynamic hand gesture recognition using hidden Markov models." Computer Science & Education (ICCSE), 2012 7th International Conference on. IEEE, 2012.
  34. Rautaray, S. S., and Agrawal, A. 2012. Real time hand gesture recognition system for dynamic applications. Int J UbiComp, 3(1), 21-31.
  35. Jiang, X., et al. "A dynamic gesture recognition method based on computer vision." Image and Signal Processing (CISP), 2013 6th International Congress on. Vol. 2. IEEE, 2013.
  36. Geetha, M., et al. "A vision based dynamic gesture recognition of Indian Sign Language on Kinect based depth images." Emerging Trends in Communication, Control, Signal Processing & Computing Applications (C2SPCA), 2013 International Conference on. IEEE, 2013.
  37. Zhang, T., and Feng, Z. 2013. Dynamic Gesture Recognition Based on Fusing Frame Images. In Intelligent Systems Design and Engineering Applications, 2013 Fourth International Conference on (pp. 280-283). IEEE.
  38. Masood, S., et al. "Dynamic time wrapping based gesture recognition."Robotics and Emerging Allied Technologies in Engineering (iCREATE), 2014 International Conference on. IEEE, 2014.
  39. Nasri, S., Behrad, A., and Razzazi, F. 2015. A novel approach for dynamic hand gesture recognition using contour-based similarity images. International Journal of Computer Mathematics, 92(4), 662-685.
  40. Peng, B., Qian, G., and Rajko, S. 2009. View-invariant full-body gesture recognition via multilinear analysis of voxel data. In Distributed Smart Cameras, 2009. ICDSC 2009. Third ACM/IEEE International Conference on (pp. 1-8). IEEE.
  41. Zhao, X., et al. "Online human gesture recognition from motion data streams."Proceedings of the 21st ACM international conference on Multimedia. ACM, 2013.
  42. Khan, R. Z., and Ibraheem, N. A. 2012. Comparative study of hand gesture recognition system. In Proc. of International Conference of Advanced Computer Science & Information Technology in Computer Science & Information Technology (CS & IT) (Vol. 2, No. 3, pp. 203-213).
  43. Wysoski, S.G., et al. "A rotation invariant approach on static-gesture recognition using boundary histograms and neural networks." Neural Information Processing, 2002. ICONIP'02. Proceedings of the 9th International Conference on. Vol. 4. IEEE, 2002.
  44. Freeman, W. T., and Roth, M. 1995. Orientation histograms for hand gesture recognition. In International workshop on automatic face and gesture recognition (Vol. 12, pp. 296-301).
  45. Hasan, M. M., and Mishra, P. K. 2011. Performance Evaluation of Modified Segmentation on Multi Block For Gesture Recognition System. International Journal of Signal Processing, Image Processing and Pattern Recognition, 4(4), 17-28.
  46. Kaushik, D. M., and Jain, R. 2014. Gesture Based Interaction NUI: An Overview.arXiv preprint arXiv:1404.2364.
  47. Sarkar, A. R., Sanyal, G., and Majumder, S. 2013. Hand gesture recognition systems: a survey. International Journal of Computer Applications (0975–8887),71(15).
  48. Starner, T., and Pentland, A. 1997. Real-time american sign language recognition from video using hidden markov models. In Motion-Based Recognition (pp. 227-243). Springer Netherlands.
  49. Starner, T., Weaver, J., and Pentland, A. 1998. Real-time american sign language recognition using desk and wearable computer based video. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 20(12), 1371-1375.
  50. Murakami, K., and Taguchi, H. 1991. Gesture recognition using recurrent neural networks. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 237-242). ACM.
  51. Maraqa, M., and Abu-Zaiter, R. 2008. Recognition of Arabic Sign Language (ArSL) using recurrent neural networks. In Applications of Digital Information and Web Technologies, 2008. ICADIWT 2008. First International Conference on the (pp. 478-481). IEEE.
  52. Kim, J. S., Jang, W., and Bien, Z. 1996. A dynamic gesture recognition system for the Korean sign language (KSL). Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 26(2), 354-359.
  53. Cho, M. G. 2006. A new gesture recognition algorithm and segmentation method of Korean scripts for gesture-allowed ink editor. Information Sciences,176(9), 1290-1303.
  54. Liang, R. H., and Ouhyoung, M. 1998. A real-time continuous gesture recognition system for sign language. In Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on (pp. 558-567). IEEE.
  55. Cipolla, R., and Hollinghurst, N. J. 1996. Human-robot interface by pointing with uncalibrated stereo vision. Image and Vision Computing, 14(3), 171-178.
  56. Bertsch, F., and Hafner, V. V. 2009. Real-time dynamic visual gesture recognition in human-robot interaction. In Humanoid Robots, 2009. Humanoids 2009. 9th IEEE-RAS International Conference on (pp. 447-453). IEEE.
  57. Portillo-Rodriguez, O., et al. "Development of a 3D real time gesture recognition methodology for virtual environment control." Robot and Human Interactive Communication, 2008. RO-MAN 2008. The 17th IEEE International Symposium on. IEEE, 2008.
  58. Guan, Y., and Zheng, M. 2008. Real-time 3D pointing gesture recognition for natural HCI. In Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on (pp. 2433-2436). IEEE.
  59. Zhang, X., et al. "Hand gesture recognition and virtual game control based on 3D accelerometer and EMG sensors." Proceedings of the 14th international conference on Intelligent user interfaces. ACM, 2009.
  60. Chambers, G.S., et al. "Hierarchical recognition of intentional human gestures for sports video annotation." Pattern Recognition, 2002. Proceedings. 16th International Conference on. Vol. 2. IEEE, 2002.
  61. Rautaray, S. S., and Agrawal, A. 2010. A vision based hand gesture interface for controlling VLC media player. International Journal of Computer Applications,10(7), 11-16.
  62. Elmezain, M., et al. "A hidden markov model-based isolated and meaningful hand gesture recognition." International Journal of Electrical, Computer, and Systems Engineering 3.3 (2009): 156-163.
  63. Freeman, W. T., and Weissman, C. 1995. Television control by hand gestures. In Proc. of Intl. Workshop on Automatic Face and Gesture Recognition (pp. 179-183).
  64. LaViola, J. 1999. A survey of hand posture and gesture recognition techniques and technology. Brown University, Providence, RI.
  65. Krueger, M. W. 1991. Artificial reality II. Addison-Wesley Professional.
  66. Weimer, D., and Ganapathy, S. K. 1992. Interaction techniques using hand tracking and speech recognition. In Multimedia interface design (pp. 109-126). ACM.
  67. Smith, G., et al. "3D scene manipulation with 2D devices and constraints." Graphics Interface. Vol. 1. 2001.
  68. Mapes, D. P., and Moshell, J. M. 1995. A two-handed interface for object manipulation in virtual environments. Presence: Teleoperators and Virtual Environments, 4(4), 403-416.
  69. Peng, B., Qian, G., and Rajko, S. 2008. View-invariant full-body gesture recognition from video. In Pattern Recognition, 2008. ICPR 2008. 19th International Conference on (pp. 1-5). IEEE.
  70. Heryadi, Y., et al. “A syntactical modelling and classification for performance evaluation of Bali traditional dance.” ICACSIS 2012
Index Terms

Computer Science
Information Sciences


Gesture recognition computer vision dynamic gesture recognition full body gesture recognition.