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A systematic literature review on vision based gesture recognition techniques

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Abstract

Human Computer Interaction (HCI) technologies are rapidly evolving the way we interact with computing devices and adapting to the constantly increasing demands of modern paradigms. One of the most useful tools in this regard is the integration of Human-to-Human Interaction gestures to facilitate communication and expressing ideas. Gesture recognition requires the integration of postures, gestures, face expressions and movements for communicating or conveying certain messages. The aim of this study is to aggregate and synthesize experiences and accumulated knowledge about Vision-Based Recognition (VBR) techniques. The major objective of conducting this Systematic Literature Review (SLR) is to highlight the state-of-the-art in the context of vision-based gesture recognition with specific focus on hand gesture recognition (HGR) techniques and enabling technologies. After a careful systematic selection process, 100 studies relevant to the four research questions were selected. This process was followed by data collection, a detailed analysis, and a synthesis of the selected studies. The results reveal that among the VBR techniques, HGR is a predominant and highly focused area of research. Research focus is also found to be converging towards sign language recognition. Potential applications of HGR techniques include desktop applications, smart environments, entertainment, sign language interpretation, virtual reality and gamification. Although various experimental research efforts have been devoted to gestures recognition, there are still numerous open issues and research challenges in this field. Lastly, considering the results from this SLR, potential future research directions are suggested, including a much needed focus on grammatical interpretation, hybrid approaches, smartphone devices, normalization, and real-life systems.

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References

  1. Alam KA, Ahmad R, Akhunzada A, Nasir MHNM, Khan SU (2015) Impact analysis and change propagation in service-oriented enterprises: a systematic review. Inf Syst 54:43–73

    Article  Google Scholar 

  2. Alon J, Athitsos V, Yuan Q, Sclaroff S (2009) A unified framework for gesture recognition and spatiotemporal gesture segmentation. IEEE Trans Pattern Anal Mach Intell 31(9):1685–1699

    Article  Google Scholar 

  3. Amin, MA, Yan H (2007) Sign language finger alphabet recognition from Gabor-PCA representation of hand gestures. In: Machine learning and cybernetics, 2007 international conference on, vol 4. IEEE, pp 2218–2223

  4. Amin O, Said H, Samy A, Mohammed HK (2016) HMM based automatic Arabic sign language translator using Kinect. In: Proceedings - 2015 10th international conference on computer engineering and systems, ICCES 2015, pp 389–392

  5. Appenrodt J, Handrich S, Al-Hamadi A, Michaelis B (2010) Multi stereo camera data fusion for fingertip detection in gesture recognition systems. In: 2010 international conference of soft computing and pattern recognition, pp 35–40

  6. Auephanwiriyakul S, Phitakwinai S, Suttapak W, Chanda P, Theera-Umpon N (2013) Thai sign language translation using scale invariant feature transform and hidden markov models. Pattern Recogn Lett 34(11):1291–1298

    Article  Google Scholar 

  7. Aujeszky T, Eid M (2016) A gesture recogintion architecture for Arabic sign language communication system. Multimed Tools Appl 75(14):8493–8511

    Article  Google Scholar 

  8. Badi H (2016) A survey on recent vision-based gesture recognition. Intell Ind Syst 2(2):179–191

    Article  Google Scholar 

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

  10. Baxter J (2000) A model of inductive bias learning. J Artif Intell Res (JAIR) 12:149–198 3

    Article  MathSciNet  MATH  Google Scholar 

  11. Bellarbi A, Benbelkacem S, Zenati-Henda N, Belhocine M (2011) Hand gesture interaction using color-based method for tabletop interfaces. In: 2011 I.E. 7th international symposium on intelligent signal processing, pp 1–6

  12. Ben Henia O, Bouakaz S (2011) 3D hand model animation with a new data-driven method. In: 2011 workshop on digital media and digital content management, pp 72–76

  13. Berbar MA, Kelash HM, Kandeel AA (2006) Faces and facial features detection in color images. In: Geometric modeling and imaging – new trends (GMAI06), pp 209–214

  14. Bilal S, Akmeliawati R, Shafie AA, Salami MJE (2011) Hidden Markov model for human to computer interaction: a study on human hand gesture recognition. Artif Intell Rev 40(4):495–516

    Article  Google Scholar 

  15. Binh ND, Shuichi E, Ejima T (2005) Real-time hand tracking and gesture recognition system. Proc. GVIP, pp 19–21

  16. 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 1–8

  17. Boulay B (2007) Human posture recognition for behaviour. PhD diss., Université Nice Sophia Antipolis

  18. Bourke AK, O’Brien JV, Lyons GM (2007) Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait Posture 26(2):194–199

    Article  Google Scholar 

  19. Bretzner L, Laptev I, Lindeberg T (2002) Hand gesture recognition using multi-scale colour features, hierarchical models and particle filtering. In: Proceedings of fifth IEEE international conference on automatic face gesture recognition, pp 405–410

  20. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):121–167

    Article  Google Scholar 

  21. Chang C-C, Chen JJ, Tai W-K, Han C-C (2006) New approach for static gesture recognition. J Inf Sci Eng 22:1047–1057

    Google Scholar 

  22. Chaudhary A, Raheja J, 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 Surv 2(1):122–133

    Article  Google Scholar 

  23. Chen F-S, Chih-Ming F, Huang C-L (2003) Hand gesture recognition using a real-time tracking method and hidden Markov models. Image Vis Comput 21(8):745–758

    Article  Google Scholar 

  24. Chen Q, Georganas ND, Petriu EM (2007) Real-time vision-based hand gesture recognition using haar-like features. In: Instrumentation and Measurement Technology Conference Proceedings, 2007. IMTC 2007. IEEE, pp 1–6

  25. Chen L, Wang F, Deng H, Ji K (2013) A survey on hand gesture recognition. In: Computer sciences and applications (CSA), 2013 International conference on. IEEE, pp 313–316

  26. Cheng J, Xie C, Bian W, Tao D (2012) Feature fusion for 3D hand gesture recognition by learning a shared hidden space. Pattern Recogn Lett 33(4):476–484

    Article  Google Scholar 

  27. Cheng H, Dai Z, Liu Z, Zhao Y (2016) An image-to-class dynamic time warping approach for both 3D static and trajectory hand gesture recognition. Pattern Recogn 55:137–147

    Article  Google Scholar 

  28. Cheok MJ, Omar Z, Jaward MH (2017) A review of hand gesture and sign language recognition techniques. Int J Mach Learn Cybern:1–23

  29. Choraś RS (2009) Hand shape and hand gesture recognition. In: Industrial electronics & applications, 2009. ISIEA 2009. IEEE symposium on, vol 1, pp 145–149. IEEE

  30. Chung WK, Wu X, Xu Y (2009) A realtime hand gesture recognition based on Haar wavelet representation. Robotics and biomimetics, 2008. ROBIO 2008. IEEE international conference on. IEEE

  31. Corera S, Krishnarajah N (2011) Capturing hand gesture movement: a survey on tools, techniques and logical considerations. Proceedings of chi sparks

  32. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  33. Côté M, Payeur P, Comeau G (2006) Comparative study of adaptive segmentation techniques for gesture analysis in unconstrained environments. In: Imagining systems and techniques, 2006. IST 2006. Proceedings of the 2006 I.E. international workshop on [imagining read imaging]. IEEE, pp 28–33

  34. Cui J, Liu Y, Xu Y, Zhao H, Zha H (2013) Tracking generic human motion via fusion of low-and high-dimensional approaches. IEEE Trans Syst Man Cybern Syst 43(4):996–1002

    Article  Google Scholar 

  35. D’Orazio T, Marani R, Renó V, Cicirelli G (2016) Recent trends in gesture recognition: how depth data has improved classical approaches. Image Vis Comput 52:56–72

    Article  Google Scholar 

  36. Dabre K, Dholay S (2014) Machine learning model for sign language interpretation using webcam images. In: 2014 International conference on circuits, systems, communication and information technology applications, CSCITA 2014, pp 317–321

  37. de Brito DM, Maracaja-Coutinho V, de Farias ST, Batista LV, do Rêgo TG (2016) A novel method to predict genomic islands based on mean shift clustering algorithm. PLoS One 11(1):e0146352

    Article  Google Scholar 

  38. de La Gorce M, 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 

  39. Deng LY (2010) Shape context based matching for hand gesture recognition. In: IET international conference on frontier computing. Theory, technologies and applications, pp 436–444

  40. Derpanis KG (2005) Mean shift clustering. Lecture notes. [Online]. Available: http://www.cse.yorku.ca/%7Ekosta/CompVis_Notes/mean_shift.pdf

  41. Derpanism KG (2004) A review of vision-based hand gestures. https://pdfs.semanticscholar.org/bc80/871a9dfb0703ece28324a80bfc051243c947.pdf. Gesture review

  42. Dinh DL, Lee S, Kim TS (2016) Hand number gesture recognition using recognized hand parts in depth images. Multimed Tools Appl 75(2):1333–1348

    Article  Google Scholar 

  43. Du H, Xiong W, Wang Z (2011) Modeling and interaction of virtual hand based on Virtools. 2011 Int. conf. multimed. technol, pp 416–419

  44. Elmezain M, Al-Hamadi A, Michaelis B (2009) Hand trajectory-based gesture spotting and recognition using HMM. In: Image processing (ICIP), 2009 16th IEEE international conference on. IEEE, pp 3577–3580

  45. Freeman WT, Roth M (1995) Orientation histograms for hand gesture recognition. In: International workshop on automatic face and gesture recognition, vol 12, pp 296–301

  46. Gameiro J, Cardoso T, Rybarczyk Y (2014) kinect-sign, teaching sign language to ‘listeners’ through a game. Procedia Technol 17:384–391

    Article  Google Scholar 

  47. Gavrila DM (1999) The visual analysis of human movement: a survey. Comput Vis Image Underst 73(1):82–98

    Article  MATH  Google Scholar 

  48. Georganas ND, Petriu EM (2008) Hand gesture recognition using haar-like features and a stochastic context-free grammar. IEEE Trans Instrum Meas 57(8):1562–1571

    Article  Google Scholar 

  49. GestureTek, 2008. [Online]. Available: http://www.gesturetek.com/

  50. Goza SM, Ambrose RO, Diftler MA, Spain IM (2004) Telepresence control of the NASA/DARPA robonaut on a mobility platform. In: Proceedings of the 2004 conference on Human factors in computing systems - CHI ‘04, pp 623–629

  51. Gupta S, Jaafar J, Ahmad WFW (2012) Static hand gesture recognition using local gabor filter. Procedia Eng 41:827–832

    Article  Google Scholar 

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

  53. HandGKET, 2011. [Online]. Available: https://sites.google.com/site/kinectapps/kinect

  54. Hasan H, Abdul-Kareem S (2014) Human–computer interaction using vision-based hand gesture recognition systems: a survey. Neural Comput Appl 25(2):251–261

    Article  Google Scholar 

  55. Hasan MM, Mishra PK (2012) Robust gesture recognition using gaussian distribution for features fitting. Int J Mach Learn Comput 2(3):266–273

    Article  Google Scholar 

  56. He G-F, Kang S-K, Song W-C, Jung S-T (2011) Real-time gesture recognition using 3D depth camera. In: 2011 I.E. 2nd international conference on software engineering and service science, pp 187–190

  57. Ho M-F, Tseng C-Y, Lien C-C, Huang C-L (2011) A multi-view vision-based hand motion capturing system. Pattern Recogn 44(2):443–453

    Article  MATH  Google Scholar 

  58. Holzmann GJ (1990) Design and validation of computer protocols. Prentice-Hall, Inc. [Online]. Available: http://cdn.worldcolleges.info/sites/default/files/x20v_1991.pdf

  59. Hsieh C-C, Liou D-H, Lee D (2010) A real time hand gesture recognition system using motion history image. In: 2010 2nd international conference on signal processing systems, vol 2, pp V2–394–V2–398

  60. Hu K, Canavan S, Yin L (2010) Hand pointing estimation for human computer interaction based on two orthogonal-views. In: Pattern recognition (ICPR), 2010 20th International Conference on. IEEE, pp 3760–3763

  61. Hu M, Shen F, Zhao J (2014) Hidden Markov models based dynamic hand gesture recognition with incremental learning method. In: 2014 international joint conference on neural networks (IJCNN), pp 3108–3115

  62. Huang D-Y, Hu W-C, Chang S-H (2009) vision-based hand gesture recognition using PCA+Gabor filters and SVM. In: 2009 fifth international conference on intelligent information hiding and multimedia signal processing, pp 1–4

  63. Huang D-Y, Hu W-C, Chang S-H (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 

  64. 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: 2011 sixth international conference on image and graphics, pp 737–742

  65. Ibarguren A, Maurtua I, Sierra B (2010) Layered architecture for real time sign recognition: Hand gesture and movement. Eng Appl Artif Intell 23(7):1216–1228

    Article  Google Scholar 

  66. Ionescu B, Coquin D, Lambert P, Buzuloiu V (2005) Dynamic hand gesture recognition using the skeleton of the hand. EURASIP J Adv Signal Process 2005(13):2101–2109

    Article  Google Scholar 

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

  68. Ionescu D, Ionescu B, Gadea C, Islam S (2011) A multimodal interaction method that combines gestures and physical game controllers. Proc. - int. conf. comput. commun. networks, ICCCN, pp 1–6

  69. Jain AK (2010) Data clustering: 50 years beyond K-means. Pattern Recogn Lett 31(8):651–666

    Article  Google Scholar 

  70. Ju SX, Black MJ, Minneman S, Kimber D (1997) Analysis of gesture and action in technical talks for video indexing. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition, pp 595–601

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

  72. Kaâniche M (2009) Gesture recognition from video sequences. PhD diss., Université Nice Sophia Antipolis

  73. Kanungo T, Mount DM, Netanyahu NS, Piatko CD, Silverman R, Wu AY (2002) An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans Pattern Anal Mach Intell 24(7):881–892

    Article  MATH  Google Scholar 

  74. Karam M (2006) PhD Thesis: a framework for research and design of gesture-based human-computer interactions. PhD diss., University of Southampton

  75. Kausar S, Javed MY (2011) A survey on sign language recognition. Front Inf Technol:95–98

  76. Kılıboz NÇ, Güdükbay U (2015) A hand gesture recognition technique for human–computer interaction. J Vis Commun Image Represent 28:97–104

    Article  Google Scholar 

  77. Kim JM, Chung K, Kang M (2016) Continuous gesture recognition using HLAC and low-dimensional space. Wirel Pers Commun 86(1):255–270

    Article  Google Scholar 

  78. Kitchenham B (2004) Procedures for performing systematic reviews. Keele, UK, Keele Univ., vol 33, no TR/SE-0401, p 28

  79. Kitchenham B, Pearl Brereton O, Budgen D, Turner M, Bailey J, Linkman S (2009) Systematic literature reviews in software engineering - a systematic literature review. Inf Softw Technol 51(1):7–15

    Article  Google Scholar 

  80. Lantz V (2011) A framework for hand gesture recognition based on accelerometer and EMG sensors. IEEE Trans Syst Man Cybern Part A Syst Hum 41(6):1064–1076

    Article  Google Scholar 

  81. Li Q, Clifford GD (2012) Dynamic time warping and machine learning for signal quality assessment of pulsatile signals. Physiol Meas 33(9):1491

    Article  Google Scholar 

  82. Li F, Wechsler H (2005) Open set recognition using transduction. IEEE Trans Pattern Anal Mach Intell 27(11):1686–1697

    Article  Google Scholar 

  83. Li W, Zhang Z, Liu Z (2010) Action recognition based on a bag of 3d points. In: Computer Vision And Pattern Recognition Workshops (CVPRW), 2010 I.E. computer society conference on. IEEE, pp 9–14

  84. Licsár A, Szirányi T (2002) Hand-gesture based film restoration. In: PRIS, pp 95–103

  85. Liu K, Kehtarnavaz N (2016) Real-time robust vision-based hand gesture recognition using stereo images. J Real-Time Image Process 11(1):201–209

    Article  Google Scholar 

  86. Liu Y, Zhang X, Cui J, Wu C, Aghajan H, Zha H (2010) Visual analysis of child-adult interactive behaviors in video sequences. In: Virtual systems and multimedia (VSMM), 2010 16th International Conference on. IEEE, pp 26–33

  87. Liu Y, Cui J, Zhao H, Zha H (2012) Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking. In: Pattern recognition (ICPR), 2012 21st international conference on. IEEE, pp 898–901

  88. Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2Activity: recognizing complex activities from sensor data. In: Yang Q, Wooldridge M (eds) Proceedings of the 24th international conference on artificial intelligence (IJCAI'15). AAAI Press, pp 1617–1623

  89. Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115

    Article  Google Scholar 

  90. Liu L, Cheng L, Liu Y, Jia Y, Rosenblum DS (2016) Recognizing complex activities by a probabilistic interval-based model. In: AAAI, vol 30, pp 1266–1272

  91. Lu W-L, Little JJ (2006) Simultaneous tracking and action recognition using the pca-hog descriptor. In: Computer and robot vision, 2006. The 3rd Canadian conference on. IEEE, pp 6

  92. Lu Y, Wei Y, Liu L, Zhong J, Sun L, Liu Y (2017) Towards unsupervised physical activity recognition using smartphone accelerometers. Multimed Tools Appl 76(8):10701–10719

    Article  Google Scholar 

  93. Luo Q, Kong X, Zeng G, Fan J (2010) Human action detection via boosted local motion histograms. Mach Vis Appl 21(3):377–389

    Article  Google Scholar 

  94. Mahdavi-Hezavehi S, Galster M, Avgeriou P (2013) Variability in quality attributes of service-based software systems: a systematic literature review. Inf Softw Technol 55(2):320–343

    Article  Google Scholar 

  95. Meng MQ, Liu PX (2003) Visual gesture recognition for human-machine interface of robot teleoperation. In: Proceedings 2003 IEEE/RSJ international conference on intelligent robots and systems (IROS 2003) (Cat. No.03CH37453), vol 2, pp 1560–1565

  96. Mgestyk, 2009. [Online]. Available: http://www.mgestyk.com/

  97. Microsoft Kinect, 2012. [Online]. Available: http://www.microsoft.com/en-us/kinectforwindows/

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

    Article  Google Scholar 

  99. Moeslund TB, Granum E (2001) A survey of computer vision-based human motion capture. Comput Vis Image Underst 81(3):231–268

    Article  MATH  Google Scholar 

  100. Moeslund TB, Hilton A, Krüger V (2006) A survey of advances in vision-based human motion capture and analysis. Comput Vis Image Underst 104(2):90–126

    Article  Google Scholar 

  101. Muñoz-Salinas R, Medina-Carnicer R, Madrid-Cuevas FJ, Carmona-Poyato A (2008) Depth silhouettes for gesture recognition. Pattern Recogn Lett 29(3):319–329

    Article  Google Scholar 

  102. Murthy G, Jadon R (2009) A review of vision based hand gestures recognition. Int J Inf Technol Knowl Manag 2:405–410

    Google Scholar 

  103. Murthy GRS, Jadon RS (2010) Hand gesture recognition using neural networks. In: Advance computing conference (IACC), 2010 I.E. 2nd international. IEEE, pp 134–138

  104. Myers BA (1998) A brief history of human-computer interaction technology. ACM Interact 5(2):44–54

    Article  Google Scholar 

  105. 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. Proc. 25th annu. int. conf. ieee eng. med. biol. soc. (IEEE Cat. No.03CH37439), vol 4, no. fig 1, pp 3286–3289

  106. Oka K, Sato Y, Koike H (2002) Real-time fingertip tracking and gesture recognition. IEEE Comput Graph Appl 22(6):64–71

    Article  Google Scholar 

  107. OMRON, 2012. [Online]. Available: http://www.omron.com/

  108. Parvini F, Shahabi C (2007) An algorithmic approach for static and dynamic gesture recognition utilising mechanical and biomechanical characteristics. Int J Bioinforma Res Appl 3(1):4–23

    Article  Google Scholar 

  109. Paulraj MP, Yaacob S, Desa H, Hema CR, Ridzuan WM, Ab Majid W (2008) Extraction of head and hand gesture features for recognition of sign language. In: Electronic design, 2008. ICED 2008. International Conference on. IEEE, pp 1–6

  110. Pavlovic VI, Sharma R, Huang TS (1997) Visual interpretation of hand gestures for human-computer interaction: a review. IEEE Trans Pattern Anal Mach Intell 19(7):677–695

    Article  Google Scholar 

  111. Pisharady PK, Saerbeck M (2015) Recent methods and databases in vision-based hand gesture recognition: a review. Comput Vis Image Underst 141:152–165

    Article  Google Scholar 

  112. Plouffe G, Cretu A (2016) Static and dynamic hand gesture recognition in depth data using dynamic time warping. IEEE Trans Instrum Meas 65(2):305–316

    Article  Google Scholar 

  113. PointGrab’s, 2012. [Online]. Available: http://www.pointgrab.com/

  114. Poppe R (2010) A survey on vision-based human action recognition. Image Vis Comput 28(6):976–990

    Article  Google Scholar 

  115. Priyal SP, Bora PK (2013) A robust static hand gesture recognition system using geometry based normalizations and Krawtchouk moments. Pattern Recogn 46(8):2202–2219

    Article  MATH  Google Scholar 

  116. Radkowski R, Stritzke C (2012) Interactive hand gesture-based assembly for augmented reality applications. Fifth int. conf. adv. comput. interact., no. c, pp 303–308

  117. Ramage D (2007) Hidden Markov models fundamentals. Lecture notes. [Online]. Available: http://cs229.stanford.edu/section/cs229-hmm.pdf

  118. Ranganath S, Ghosh D, Kevin NYY (2004) Trajectory modeling in gesture recognition using cybergloves and magnetic trackers. Proc. 2004 I.E. Reg. 10 Conf. TENCON, vol 1, pp 571–574

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

    Article  Google Scholar 

  120. Rautaray SS, Agrawal A (2010) A novel human computer interface based on hand gesture recognition using computer vision techniques. In: Proceedings of the first international conference on intelligent interactive technologies and multimedia IITM ‘10. ACM, pp 292–296

  121. 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 

  122. Reese MG (2001) Application of a time-delay neural network to promoter annotation in the Drosophila melanogaster genome. Comput Chem 26(1):51–56

    Article  Google Scholar 

  123. Ren Y, Zhang F (2009) Hand gesture recognition based on MEB-SVM. In: Embedded software and systems, 2009. ICESS'09. International conference on. IEEE, pp 344–349

  124. Ren Z, Yuan J, Meng J, Zhang Z (2013) Robust part-based hand gesture recognition using kinect sensor. IEEE Trans Multimedia 15(5):1110–1120

    Article  Google Scholar 

  125. S.E. Group (2007) Guidelines for performing systematic literature reviews in software engineering

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

  127. Sangineto E, Cupelli M (2012) Real-time viewpoint-invariant hand localization with cluttered backgrounds. Image Vis Comput 30(1):26–37

    Article  Google Scholar 

  128. Schlömer T, Poppinga B, Henze N, Boll S (2008) Gesture recognition with a Wii controller. In: Proceedings of the 2nd international conference on Tangible and embedded interaction - TEI ‘08, p 11

  129. Senin P (2008) Dynamic time warping algorithm review. Science (80- ) 2007:1–23

    Google Scholar 

  130. Shaily S, Mangat V (2015). The hidden Markov model and its application to human activity recognition. In: Recent advances in engineering & computational sciences (RAECS), 2015 2nd international conference on. IEEE, pp 1–4

  131. Sharath Kumar YH, Vinutha V (2016) Hand gesture recognition for sign language: a skeleton approach. In: Das S, Pal T, Kar S, Satapathy SC, Mandal JK (eds) Proceedings of the 4th international conference on frontiers in intelligent computing: theory and applications (FICTA) 2015. Springer India, New Delhi, pp 611–623

    Chapter  Google Scholar 

  132. Sharma R, Huang TS, Pavlovic VI, Chu S, Schul K (1996) Speech/gesture interface to a visual computing environment for molecular biologists. In: Proceedings of 13th international conference on pattern recognition, vol 3, pp 964–968

  133. Smith GM, Schraefel MC (2004) The radial scroll tool: scrolling support for stylus- or touch-based document navigation. In Proceedings of the 17th annual ACM symposium on User interface software and technology - UIST ‘04, p 53

  134. SoftKinetic, IISU SDK, 2012 [Online]. Available: http://www.softkinetic.com/support/Forum/aft/746. Accessed 02 Dec 2015

  135. Starner T, Pentland A (1997) Real-time american sign language recognition from video using hidden markov models. In: Motion-based recognition. Springer Netherlands, pp 227–243

  136. Starner T, Weaver J, Pentland A (1998) Real-time american sign language recognition using desk and wearable computer based video. IEEE Trans Pattern Anal Mach Intell 20(12):1371–1375

    Article  Google Scholar 

  137. Starner T, Auxier J, Ashbrook D, Gandy M (2000) The gesture pendant: a self-illuminating, wearable, infrared computer vision system for home automation control and medical monitoring. In: Wearable computers, the fourth international symposium on. IEEE, pp 87–94

  138. Stergiopoulou E, Papamarkos N (2009) Hand gesture recognition using a neural network shape fitting technique. Eng Appl Artif Intell 22(8):1141–1158

    Article  Google Scholar 

  139. Stotts D, Smith JM, Gyllstrom K (2004) FaceSpace: endo- and exo-spatial hypermedia in the transparent video facetop. In: Proceedings of the fifteenth ACM conference on hypertext & hypermedia - HYPERTEXT ‘04, p 48

  140. Suk H-I, Sin B-K, Lee S-W Robust modeling and recognition of hand gestures with dynamic Bayesian network. In: Proceedings of 19th IAPR/IEEE international conference on pattern recognition, Tampa, USA, December 2008, pp 1–4

  141. Suk H-I, Sin B-K, Lee S-W (2010) Hand gesture recognition based on dynamic Bayesian network framework. Pattern Recogn 43(9):3059–3072

    Article  MATH  Google Scholar 

  142. Swapna B, Pravin F, Dharaskar Rajiv V (2011) Hand gesture recognition system for numbers using thresholding. In: Communications in computer and information science, vol 250 CCIS, pp 782–786

    Google Scholar 

  143. Symeonidis K (1996) Hand gesture recognition using neural networks. Neural Netw 13:5.1

    Google Scholar 

  144. Tan T, Guo Z (2011) Research of hand positioning and gesture recognition based on binocular vision. In: 2011 I.E. international symposium on VR innovation, pp 311–315

  145. Thirumuruganathan S (2010) A detailed introduction to K-nearest neighbor (KNN) algorithm. Retrieved on July 21 (2010): 2015. [Online]. Available: https://saravananthirumuruganathan.wordpress.com/2010/05/17/a-detailed-introduction-to-k-nearest-neighbor-knn-algorithm/

  146. 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 

  147. Triesch J, von der Malsburg C (1996) Robust classification of hand postures against complex backgrounds. In: Proceedings of the second international conference on automatic face and gesture recognition, pp 170–175

  148. Triesch J, Von Der Malsburg C (2001) A system for person-independent hand posture recognition against complex backgrounds. IEEE Trans Pattern Anal Mach Intell 23(12):1449–1453

    Article  Google Scholar 

  149. Tubaiz N, Shanableh T, Assaleh K (2015) Glove-based continuous Arabic sign language recognition in user-dependent mode. IEEE Trans Human-Machine Syst 45(4):526–533

    Article  Google Scholar 

  150. Vafadar M, Behrad A (2015) A vision based system for communicating in virtual reality environments by recognizing human hand gestures. Multimed Tools Appl 74(18):7515–7535

    Article  Google Scholar 

  151. Van den Bergh M, Van Gool L (2011) Combining RGB and ToF cameras for real-time 3D hand gesture interaction. In: 2011 I.E. workshop on applications of computer vision (WACV), pp 66–72

  152. Varkonyi-Koczy 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 

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

  154. Vogler C, Metaxas D (1998) ASL recognition based on a coupling between HMMs and 3D motion analysis. In: Computer vision, 1998. Sixth international conference on. IEEE, pp 363–369

  155. Wachs JP, Kölsch M, Stern H, Edan Y (2011) Vision-based hand-gesture applications. Commun ACM 54(2):60

    Article  Google Scholar 

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

    Google Scholar 

  157. Webel S, Keil J, Zoellner M (2008) Multi-touch gestural interaction in X3D using hidden Markov models. In: Proceedings of the 2008 ACM symposium on virtual reality software and technology. ACM, pp 263–264

  158. 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 

  159. Wu Y, Huang TS (1999) Vision-based gesture recognition: a review. In: Gesture workshop, vol 1739, pp 103–115

    Chapter  Google Scholar 

  160. Wu C-H, Chen W-L, Lin CH (2016) Depth-based hand gesture recognition. Multimed Tools Appl 75(12):7065–7086

    Article  Google Scholar 

  161. Yadav K, Bhattacharya J (2016) Real-time hand gesture detection and recognition for human computer interaction. In: Berretti S, Thampi S, Srivastava P (eds) Intelligent systems technologies and applications. Advances in intelligent systems and computing, vol 384. Springer, Cham

    Google Scholar 

  162. Yang J, Xu J, Li M, Zhang D, Wang C (2011) A real-time command system based on hand gesture recognition. In: 2011 seventh international conference on natural computation, vol 3, pp 1588–1592

  163. Yeo H-S, Lee B-G, Lim H (2015) Hand tracking and gesture recognition system for human-computer interaction using low-cost hardware. Multimed Tools Appl 74(8):2687–2715

    Article  Google Scholar 

  164. Yun L, Peng Z (2009) An automatic hand gesture recognition system based on Viola-Jones method and SVMs. In: Computer science and engineering, 2009. WCSE'09. Second International Workshop on, vol 2. IEEE, pp 72–76

  165. Zhou Y, Jiang G, Lin Y (2016) A novel finger and hand pose estimation technique for real-time hand gesture recognition. Pattern Recogn 49:102–114

    Article  Google Scholar 

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Acknowledgments

We would like to extend our appreciation to University of Malaya and The University of Jordan for providing all necessary support to conduct this research work.

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Authors and Affiliations

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

    Ahmad Sami Al-Shamayleh, Rodina Ahmad, Khubaib Amjad Alam & Nazean Jomhari

  2. Department of Computer Information Systems, King Abdullah II School of Information Technology, The University of Jordan, Amman, 11942, Jordan

    Mohammad A. M. Abushariah

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Correspondence to Ahmad Sami Al-Shamayleh.

Appendix

Appendix

This SLR was aimed to aggregate and synthesize accumulated knowledge and experience in the context of gesture recognition and vision-based hand gesture recognition. Topics discussed included hand gesture analysis approaches, taxonomies of hand gesture recognition approaches, techniques and algorithms, a taxonomy of open issues and challenges, as well as potential technological improvements, and future research trends and directions. Publications in the hand gesture recognition field were investigated and scanned widely, including accredited scientific journals, conference proceedings, workshops and online reports. The investigation was carried out in English. All publications evaluated were split into four categories, as shown in Fig. 21. For this research, the proportion of scientific journals perused was 60% of all references employed. In addition, the proportion of conference proceedings was 36%. Finally, workshop papers and theses comprised 2% of all references used. Figure 22 presents a chronological distribution of studies over the years. Figure 23 illustrates a statistical analysis of hand gesture recognition solutions according to contribution type.

Fig. 21
figure 21

References by category specifically dedicated to gesture recognition from over 100 scientific documents evaluated

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Fig. 22
figure 22

Chronological distribution of studies over the years

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Fig. 23
figure 23

Statistical analysis of hand gesture recognition solutions according to contribution type

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Table 21 Studies used to answer the research questions on hand gestures recognition from 1st January 1996 to 31th October 2016
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Table 22 Studies distribution per publication sources
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Al-Shamayleh, A.S., Ahmad, R., Abushariah, M.A.M. et al. A systematic literature review on vision based gesture recognition techniques. Multimed Tools Appl 77, 28121–28184 (2018). https://doi.org/10.1007/s11042-018-5971-z

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