Top

Neural Computing and Applications

Published in:

01-08-2014 | Review

RETRACTED ARTICLE: Human–computer interaction using vision-based hand gesture recognition systems: a survey

Authors: Haitham Hasan, Sameem Abdul-Kareem

Published in: Neural Computing and Applications | Issue 2/2014

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

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.

next article Swarm robots reinforcement learning convergence accuracy-based learning classifier systems with gradient descent (XCS-GD)

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

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

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

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

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

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

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

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

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

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):122–133CrossRef

10.

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

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

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 CrossRef

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):1531–1536CrossRef

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):4–37CrossRef

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 33:476–484CrossRef

37.

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

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):178–187CrossRef

41.

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

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):474–486CrossRef

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):1505–1514CrossRef

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):1793–1805CrossRef

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 44:443–453CrossRefMATH

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):6031–6042CrossRef

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

Title: RETRACTED ARTICLE: Human–computer interaction using vision-based hand gesture recognition systems: a survey
Authors: Haitham Hasan
Sameem Abdul-Kareem
Publication date: 01-08-2014
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 2/2014
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-013-1481-0

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 2/2014

Hybrid krill herd algorithm with differential evolution for global numerical optimization

Adaptive subspace learning: an iterative approach for document clustering

A linear hybrid methodology for improving accuracy of time series forecasting

Predicting phishing websites based on self-structuring neural network

Adaptive neural control and learning of affine nonlinear systems

Game-theoretic approach to cooperative control of distributed energy resources in islanded microgrid considering voltage and frequency stability

Premium Partner