Skip to main content
SpringerLink
Log in

RETRACTED ARTICLE: Human–computer interaction using vision-based hand gesture recognition systems: a survey

  • Review
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

This article was retracted on 15 February 2017

Abstract

Considerable effort has been put toward the development of intelligent and natural interfaces between users and computer systems. In line with this endeavor, several modes of information (e.g., visual, audio, and pen) that are used either individually or in combination have been proposed. The use of gestures to convey information is an important part of human communication. Hand gesture recognition is widely used in many applications, such as in computer games, machinery control (e.g., crane), and thorough mouse replacement. Computer recognition of hand gestures may provide a natural computer interface that allows people to point at or to rotate a computer-aided design model by rotating their hands. Hand gestures can be classified into two categories: static and dynamic. The use of hand gestures as a natural interface serves as a motivating force for research on gesture taxonomy, its representations, and recognition techniques. This paper summarizes the surveys carried out in human--computer interaction (HCI) studies and focuses on different application domains that use hand gestures for efficient interaction. This exploratory survey aims to provide a progress report on static and dynamic hand gesture recognition (i.e., gesture taxonomies, representations, and recognition techniques) in HCI and to identify future directions on this topic.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Just A (2006) Two-handed gestures for human–computer interaction. Research report IDIAP 06-73, EPFL

  2. Hasan H, Abdul Kareem S (2012) Fingerprint image enhancement and recognition algorithms: a survey. Neural Comput Appl. doi:10.1007/s00521-012-1113-0

  3. Hasan H, Abdul Kareem S (2012) Static hand gesture recognition using neural networks. Artif Intell Rev. doi:10.1007/s10462-011-9303-1

  4. Karam M (2006) A framework for research and design of gesture-based human–computer interactions. PhD Thesis, University of Southampton

  5. Ionescu B, Coquin D, Lambert P, Buzuloiu V (2005) Dynamic hand gesture recognition using the skeleton of the hand. EURASIP J Appl Signal Process 2101–2109

  6. Moeslund T, Granum E (2001) A survey of computer vision based human motion capture. Comput Vis Image Underst 81:231–268

    Article  MATH  Google Scholar 

  7. Derpanis KG (2004) A review of vision- based hand gestures. http://cvr.yorku.ca/members/gradstudents/kosta/publications/fileGesturereview.pdf

  8. Mitra S, Acharya T (2007) Gesture recognition: a survey. IEEE Trans Syst Man Cybern, Part C Appl Rev 37(3):311–324

    Article  Google Scholar 

  9. Chaudhary A, Raheja JL, Das K, Raheja S (2011) Intelligent approaches to interact with machines using hand gesture recognition in natural way: a survey. Int J Comput Sci Eng Survey (IJCSES) 2(1):122–133

    Article  Google Scholar 

  10. Wachs JP, Kolsch M, Stern H, Edan Y (2011) Vision-based hand-gesture applications. Commun ACM 54:60–71

    Article  Google Scholar 

  11. Corera S, Krishnarajah N (2011) Capturing hand gesture movement: a survey on tools, techniques and logical considerations. In: Proceedings of chi sparks 2011 HCI research, innovation and implementation, Arnhem, Netherlands. http://proceedings.chi-sparks.nl/documents/Education-Gestures/FP-35-AC-EG.pdf

  12. Kevin NYY, Ranganath S, Ghosh D (2004) Trajectory modeling in gesture recognition using CyberGloves, TENCON 2004, IEEE region 10 conference

  13. Symeonidis K (1996) Hand gesture recognition using neural networks. Neural Netw 13:1–5

    Google Scholar 

  14. Kanniche MB (2009) Gesture recognition from video sequences. PhD Thesis, University of Nice 2009

  15. Webel S, Keil J, Zoellner M (2008) Multi-touch gestural interaction in X3D using hidden markov models. In: VRST 08 Proceedings of the 2008 ACM symposium on virtual reality software and technology. ACM, New York, NY, USA, pp 263–264

  16. Schlomer T, Poppinga B, Henze N, Boll S (2008) Gesture recognition with a wii controller. In: TEI 08 Proceedings of the 2nd international conference on tangible and embedded interaction. ACM, New York, NY, USA, pp 11–14

  17. Bourke A, Brien JO, Lyons G (2007) Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait Posture 26(2):194–199. http://www.sciencedirect.com/science/article/B6T6Y-4MBCJHV-1/2/f87e4f1c82f3f93a3a5692357e3fe00c

    Article  Google Scholar 

  18. Noury N, Barralon P, Virone G, Boissy P, Hamel M, Rumeau P (2003) A smart sensor based on rules and its evaluation in daily routines. In: Engineering in medicine and biology society, 2003. Proceedings of the 25th annual international conference of the IEEE, vol. 4, pp 3286–3289

  19. Hall ET (1973) The silent language, Anchor Books. ISBN-13 978-0385055499

  20. McNeill D (1992) Hand and mind: what gestures reveal about thought. University of Chicago Press. ISBN 9780226561325

  21. Boulay B (2007) Human posture recognition for behavior understanding. PhD thesis, Universite de Nice-Sophia Antipolis

  22. Bretzner L, Laptev I, Lindeberg T (2002) Hand gesture recognition using multi-scale colour features, hierarchical models and particle filtering. In: Fifth IEEE international conference on automatic face and gesture recognition, pp 405–410. doi:10.1109/AFGR.2002.1004190

  23. Birdal A, Hassanpour R (2008) Region based hand gesture recognition. In: 16th international conference in central Europe on computer graphics, visualization and computer vision, pp 17

  24. Ju SX, Black MJ, Minneman S, Kimber D (1997) Analysis of gesture and action in technical talks for video indexing. Technical report. American Association for Artificial Intelligence. AAAI Technical Report SS-97-03

  25. Luo Q, Kong X, Zeng G, Fan J (2008) Human action detection via boosted local motion histograms. Mach Vis Appl. doi:10.1007/s00138-008-0168-5

  26. Lindsay J (2009) K-means classifier tutorial. http://www.uoguelph.ca/~hydrogeo/Whitebox/Help/kMeansClass.html

  27. Thirumuruganathan S (2010) A detailed introduction to K-nearest neighbor (KNN) algorithm. http://saravananthirumuruganathan.wordpress.com/2010/05/17/a-detailed-introduction-to-k-nearest-neighbor-knnalgorithm/

  28. Derpanis KG (2005) Mean shift clustering, lecture notes. http://www.cse.yorku.ca/kosta/CompVisNotes/meanshift.pdf

  29. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Kluwer Academic, Boston, pp 1–43

  30. Ramage D (2007) Hidden Markov models fundamentals. Lecture notes. http://cs229.stanford.edu/section/cs229-hmm.pdf

  31. Senin P (2008) Dynamic time warping algorithm review. Technical report. http://csdl.ics.hawaii.edu/techreports/08-04/08-04.pdf

  32. Wohler C, Anlauf JK (1999) An adaptable time-delay neural-network algorithm for image sequence analysis. IEEE Trans Neural Netw 10(6):1531–1536

    Article  Google Scholar 

  33. Holzmann GJ (1991) Design and validation of computer protocols. Prentice Hall, New Jersey

  34. Haykin SS (2009) Neural networks and learning machines. Prentice Hall, New York

  35. Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. Pattern Anal Mach Intell, IEEE Trans 22(1):4–37

    Article  Google Scholar 

  36. Cheng J, Xie X, Bian W, Tao D (2012) Feature fusion for 3D hand gesture recognition by learning a shared hidden space. Pattern Recogn Lett 33:476–484

    Article  Google Scholar 

  37. Sangineto EE, Cupelli M (2012) Real-time viewpoint-invariant hand localization with cluttered backgrounds. Image Vis Comput 30:26–37

    Article  Google Scholar 

  38. Wang GW, Zhang C, Zhuang J (2012) An application of classifier combination methods in hand gesture recognition, mathematical problems in engineering, vol 2012. Hindawi Publishing Corporation, pp 1–17. doi:10.1155/2012/346951

  39. Radkowski R, Stritzke C (2012) Interactive hand gesture-based assembly for augmented reality applications. In: ACHI: The fifth international conference on advances in computer–human interactions, IARIA, pp 303–308

  40. Tran C, Trivedi MM (2012) 3-D posture and gesture recognition for interactivity in smart spaces. IEEE Trans Ind Inf 8(1):178–187

    Article  Google Scholar 

  41. Rautaray SS, Agrawal A (2012) Real time hand gesture recognition system for dynamic applications. Int J UbiComp 3(1):21–31

    Article  Google Scholar 

  42. Reale MJ, Canavan S, Yin L, Hu K, Hung T (2011) A multi-gesture interaction system using a 3-D Iris disk model for gaze estimation and an active appearance model for 3-D hand pointing. IEEE Trans Multimed 13(3):474–486

    Article  Google Scholar 

  43. Vsrkonyi-Kczy AR, Tusor B (2011) Human–computer interaction for smart environment applications using fuzzy hand posture and gesture models. IEEE Trans Instrum Meas 60(5):1505–1514

    Article  Google Scholar 

  44. Gorce MDL, Fleet DJ, Paragios N (2011) Model-based 3D hand pose estimation from monocular video. IEEE Trans Pattern Anal Mach Intell 33(9):1793–1805

    Article  Google Scholar 

  45. Sajjawiso T, Kanongchaiyos P (2011) 3D hand pose modeling from uncalibrate monocular images. In: Eighth international joint conference on computer science and software engineering (JCSSE), pp 177–181

  46. Henia OB, Bouakaz S (2011) 3D Hand model animation with a new data-driven method. In: Workshop on digital media and digital content management, IEEE, pp 72–76

  47. Ionescu D, Ionescu B, Gadea C, Islam S (2011) A multimodal interaction method that combines gestures and physical game controllers. In: Proceedings of 20th international conference on computer communications and networks (ICCCN), IEEE, pp 1–6

  48. Yang J, Xu J, Li M, Zhang D, Wang C (2011) A real-time command system based on hand gesture recognition. In: Seventh international conference on natural computation, pp 1588–1592

  49. Huang D, Tang W, Ding Y, Wan T, Wu X, Chen Y (2011) Motion capture of hand movements using stereo vision for minimally invasive vascular interventions. In: Sixth international conference on image and graphics, pp 737–742

  50. Ionescu D, Ionescu B, Gadea C, Islam S (2011) An intelligent gesture interface for controlling TV sets and set-top boxes. In: 6th IEEE international symposium on applied computational intelligence and informatics, pp 159–164

  51. Bergh M, Gool L (2011) Combining RGB and ToF cameras for real-time 3D hand gesture interaction. In: Workshop on applications of computer vision (WACV), IEEE, pp 66–72

  52. Bao J, Song A, Guo Y, Tang H (2011) Dynamic hand gesture recognition based on SURF tracking in international conference on electric information and control engineering (ICEICE), pp 338–341

  53. Bellarbi A, Benbelkacem S, Zenati-Henda N, Belhocine M (2011) Hand gesture interaction using color-based method for tabletop interfaces. In: IEEE 7th international symposium on intelligent signal processing (WISP), pp 16

  54. Hackenberg G, McCall R, Broll W (2011) Lightweight palm and finger tracking for real-time 3D gesture control. In: IEEE virtual reality conference (VR), pp 19–26

  55. Du H, Xiong W, Wang Z (2011) Modeling and interaction of virtual hand based on virtools. In: International conference on multimedia technology (ICMT), pp 416–419

  56. Rautaray SS, Agrawal A (2011) A novel human–computer interface based on hand gesture recognition using computer vision techniques. In: International conference on intelligent interactive technologies and multimedia (IITM-2011), pp 292–296

  57. Visser M, Hopf V (2011) Near and far distance gesture tracking for 3D applications In: 3DTV conference: the true vision-capture, transmission and display of 3D Vedio (3DTV-CON), pp 1–4

  58. He GF, Kang SK, Song WC, Jung ST (2011) Real-time gesture recognition using 3D depth camera. In: 2nd international conference on software engineering and service science (ICSESS), pp 187–190

  59. Tan T, De Geo ZM (2011) Research of hand positioning and gesture recognition based on binocular vision. In: EEE international symposium on virtual reality innovation 2011, pp 311–315

  60. Ho MF, Tseng CY, Lien CC, Huang CL (2011) A multi-view vision- based hand motion capturing system. Pattern Recogn 44:443–453

    Article  MATH  Google Scholar 

  61. Huang DY, Hu WC, Chang SH (2011) Gabor filter-based hand-pose angle estimation for hand gesture recognition under varying illumination. Expert Syst Appl 38(5):6031–6042

    Article  Google Scholar 

  62. Hsieh CC, Liou DH , Lee D (2010) A real time hand gesture recognition system using motion history image. In: 2nd international conference on signal processing systems (ICSPS), pp 394–398

  63. IISU SDK (2012) http://www.softkinetic.com/Solutions/iisuSDK.aspx

  64. Hand GKET (2011) http://sites.google.com/site/kinectapps/kinect

  65. Mgestyk (2009) http://www.mgestyk.com/

  66. Wii Nintendo (2006) http://www.nintendo.com/wii

  67. Hand Vu (2003) http://www.movesinstitute.org/~kolsch/HandVu/HandVu.html

Download references

Author information

Authors and Affiliations

  1. Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Haitham Hasan & Sameem Abdul-Kareem

Authors
  1. Haitham Hasan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sameem Abdul-Kareem
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haitham Hasan.

Additional information

An erratum to this article can be found online at http://dx.doi.org/10.1007/s00521-017-2867-1.

The Editor-in-Chief and the Publisher retract the above-mentioned article due to self-plagiarism. The article has significant overlap with two other publications by the same co-author:

Haitham Badi, Mohammed Fadhel, Sana Sabry, Mohamed Jasem, A Survey on Human–Computer Interaction Technologies and Techniques, International Journal of Data Science and Analytics (2016) 2:1. doi: 10.1007/s41060-016-0018-x

Haitham Badi, A Survey on Recent Vision-Based Gesture Recognition, Intelligent Industrial Systems (2016) 2:2. doi:10.1007/s40903-016-0046-9

About this article

Cite this article

Hasan, H., Abdul-Kareem, S. RETRACTED ARTICLE: Human–computer interaction using vision-based hand gesture recognition systems: a survey. Neural Comput & Applic 25, 251–261 (2014). https://doi.org/10.1007/s00521-013-1481-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-013-1481-0

Keywords

Search

Navigation