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A review of hand gesture and sign language recognition techniques

  • Ming Jin CheokEmail author
  • Zaid Omar
  • Mohamed Hisham Jaward
Original Article

Abstract

Hand gesture recognition serves as a key for overcoming many difficulties and providing convenience for human life. The ability of machines to understand human activities and their meaning can be utilized in a vast array of applications. One specific field of interest is sign language recognition. This paper provides a thorough review of state-of-the-art techniques used in recent hand gesture and sign language recognition research. The techniques reviewed are suitably categorized into different stages: data acquisition, pre-processing, segmentation, feature extraction and classification, where the various algorithms at each stage are elaborated and their merits compared. Further, we also discuss the challenges and limitations faced by gesture recognition research in general, as well as those exclusive to sign language recognition. Overall, it is hoped that the study may provide readers with a comprehensive introduction into the field of automated gesture and sign language recognition, and further facilitate future research efforts in this area.

Keywords

Computer vision Gesture recognition Image processing Machine learning Sign language 

Notes

Acknowledgements

This research was made possible by the funding of the Ministry of Higher Education Malaysia and Universiti Teknologi Malaysia through the Research University Tier 1 Grant (Vote No. 09H75).

Compliance with ethical standards

Funding

This study was funded by Ministry of Higher Education Malaysia and Universiti Teknologi Malaysia through the Research University Tier 1 Grant (Vote No. 09H75).

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    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 20:1371–1375Google Scholar
  2. 2.
    Starner T, Pentland A (1997) Real-time American sign language recognition from video using hidden Markov models. In: Motion-based recognition. Springer, pp 227–243Google Scholar
  3. 3.
    Lockton R (2002) Hand gesture recognition using computer vision 4th year project report, pp 1–69Google Scholar
  4. 4.
    Lee J, Lee Y, Lee E, Hong S (2004) Hand region extraction and gesture recognition from video stream with complex background through entropy analysis. In: Engineering in Medicine and Biology Society, 2004. IEMBS’04. 26th annual international conference of the IEEE, IEEE, pp 1513–1516Google Scholar
  5. 5.
    Binh ND, Ejima T (2005) Hand gesture recognition using fuzzy neural network. In: Proc. ICGST conf. graphics, vision and image process, Cairo. pp 1–6Google Scholar
  6. 6.
    Shin J-H, Lee JS, Kil SK, Shen DF, Ryu JG, Lee EH, Min HK, Hong SH (2006) Hand region extraction and gesture recognition using entropy analysis. IJCSNS Int J Comput Sci Netw Secur 6:216–222Google Scholar
  7. 7.
    Bay H, Tuytelaars T, Van Gool L (2006) Surf: speeded up robust features. In: European conference on computer vision. Springer, pp 404–417Google Scholar
  8. 8.
    Chakraborty P, Sarawgi P, Mehrotra A, Agarwal G, Pradhan R (2008) Hand gesture recognition: a comparative study. In: Proceedings of the international multiconference of engineers and computer scientists, Citeseer, pp 19–21Google Scholar
  9. 9.
    Zhang Q, Chen F, Liu X (2008) Hand gesture detection and segmentation based on difference background image with complex background. In: Embedded software and systems, 2008. ICESS’08. International conference, IEEE, pp 338–343Google Scholar
  10. 10.
    Elmezain M, Al-Hamadi A, Michaelis B (2008) Real-time capable system for hand gesture recognition using Hidden Markov models in stereo color image sequences. J WSCG 16(1–3):65–72Google Scholar
  11. 11.
    Kim D, Dahyot R (2008) Face components detection using SURF descriptors and SVMs. In: Machine vision and image processing conference, 2008. IMVIP’08 international, IEEE, pp 51–56Google Scholar
  12. 12.
    Rokade R, Doye D, Kokare M (2009) Hand gesture recognition by thinning method. In: Digital image processing, 2009 international conference, IEEE, pp 284–287Google Scholar
  13. 13.
    Appenrodt J, Al-Hamadi A, Michaelis B (2010) Data gathering for gesture recognition systems based on single color-, stereo color-and thermal cameras. Int J Signal Process Image Process Pattern Recognit 3:37–50Google Scholar
  14. 14.
    Hasan MM, Misra PK (2011) HSV brightness factor matching for gesture recognition system. IJIP 4(5):456–467Google Scholar
  15. 15.
    Dardas NH, Georganas ND (2011) Real-time hand gesture detection and recognition using bag-of-features and support vector machine techniques. IEEE Trans Instrum Meas 60:3592–3607Google Scholar
  16. 16.
    Schmitt D, McCoy N (2011) Object classification and localization using SURF descriptors. CS 229:1–5Google Scholar
  17. 17.
    Ghotkar AS, Kharate GK (2012) Hand segmentation techniques to hand gesture recognition for natural human computer interaction. Int J Hum Comput Interact IJHCI 3:15Google Scholar
  18. 18.
    Lionnie R, Timotius IK, Setyawan I (2012) Performance comparison of several pre-processing methods in a hand gesture recognition system based on nearest neighbor for different background conditions. J ICT Res Appl 6:183–194Google Scholar
  19. 19.
    Pansare JR, Gawande SH, Ingle M (2012) Real-time static hand gesture recognition for American sign language (ASL) in complex background. J Signal Inf Process 3:364Google Scholar
  20. 20.
    Pansare JR, Dhumal H, Babar S, Sonawale K, Sarode A (2013) Real time static hand gesture recognition system in complex background that uses number system of Indian sign language. Int J Adv Res Comput Eng Technol IJARCET 2:1086–1090Google Scholar
  21. 21.
    Rajathi P, Jothilakshmi S (2013) A static Tamil sign language recognition system. Int J Adv Res Comput Commun Eng 2(4):1–7Google Scholar
  22. 22.
    Chai X, Li G, Lin Y, Xu Z, Tang Y, Chen X, Zhou M (2013) Sign language recognition and translation with kinect. In: IEEE Conf, AFGRGoogle Scholar
  23. 23.
    Tharwat A, Gaber T, Hassanien AE, Shahin M, Refaat B (2015) Sift-based arabic sign language recognition system. In: Afro-European conference for industrial advancement, Springer, pp 359–370Google Scholar
  24. 24.
    Ahsan MR, Ibrahimy MI, Khalifa OO (2011) Electromygraphy (EMG) signal based hand gesture recognition using artificial neural network (ANN). In: Mechatronics (ICOM), 2011 4th international conference, IEEE, pp 1–6Google Scholar
  25. 25.
    Yun L, Lifeng Z, Shujun Z (2012) A hand gesture recognition method based on multi-feature fusion and template matching. Procedia Eng 29:1678–1684Google Scholar
  26. 26.
    Rekha J, Bhattacharya J, Majumder S (2011) Hand gesture recognition for sign language: a new hybrid approach. In: Proc. conference on image processing computer vision and pattern recognition, pp 1–7Google Scholar
  27. 27.
    Akmeliawati R, Dadgostar F, Demidenko S, Gamage N, Kuang YC, Messom C, Ooi M, Sarrafzadeh A, SenGupta G (2009) Towards real-time sign language analysis via markerless gesture tracking. In: Instrumentation and measurement technology conference, I2MTC’09, IEEE, pp 1200–1204Google Scholar
  28. 28.
    Vogler C, Metaxas D (1999) Parallel hidden markov models for american sign language recognition. In: The Proceedings of the seventh IEEE international conference, IEEE, pp 116–122Google Scholar
  29. 29.
    Wang X, Xia M, Cai H, Gao Y, Cattani C (2012) Hidden-Markov-models-based dynamic hand gesture recognition. Math Prob Eng 2012:986134. doi: 10.1155/2012/986134 MathSciNetzbMATHGoogle Scholar
  30. 30.
    Starner TE (1995) Visual recognition of American sign language using hidden Markov models. Dept of Brain and Cognitive Sciences, Massachusetts Inst of Tech, CambridgeGoogle Scholar
  31. 31.
    Wilson AD, Bobick AF (1999) Parametric hidden Markov models for gesture recognition. IEEE Trans Pattern Anal Mach Intell 21:884–900Google Scholar
  32. 32.
    Vogler C, Metaxas D (2001) A framework for recognizing the simultaneous aspects of American sign language. Comput Vision Image Underst 81:358–384zbMATHGoogle Scholar
  33. 33.
    Chen F-S, Fu C-M, Huang C-L (2003) Hand gesture recognition using a real-time tracking method and hidden Markov models. Image Vis Comput 21:745–758Google Scholar
  34. 34.
    Bao J, Song A, Guo Y, Tang H (2011) Dynamic hand gesture recognition based on SURF tracking. In: Electric information and control engineering (ICEICE), international conference, IEEE, pp 338–341Google Scholar
  35. 35.
    Kim J, Mastnik S, André E (2008) EMG-based hand gesture recognition for realtime biosignal interfacing. In: Proceedings of the 13th international conference on Intelligent user interfaces, ACM, pp 30–39Google Scholar
  36. 36.
    Jones MJ, Rehg JM (2002) Statistical color models with application to skin detection. Int J Comput Vis 46:81–96zbMATHGoogle Scholar
  37. 37.
    Murthy G, Jadon R (2009) A review of vision based hand gestures recognition. Int J Inf Technol Knowl Manag 2:405–410Google Scholar
  38. 38.
    Chaudhary A, Raheja JL, Das K, Raheja S (2013) Intelligent approaches to interact with machines using hand gesture recognition in natural way: a survey. arXiv preprint arXiv:13032292Google Scholar
  39. 39.
    Khan RZ, Ibraheem NA (2012) Survey on gesture recognition for hand image postures. Comput Inf Sci 5:110Google Scholar
  40. 40.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110Google Scholar
  41. 41.
    Kim J-S, Jang W, Bien Z (1996) A dynamic gesture recognition system for the Korean sign language (KSL) IEEE Trans Syst Man Cybern Part B Cybern 26:354–359Google Scholar
  42. 42.
    Liang R-H, 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, IEEE, pp 558–567Google Scholar
  43. 43.
    Delac K, Grgic M, Grgic S (2005) Independent comparative study of PCA, ICA, and LDA on the FERET data set. Int J Imaging Syst Technol 15(5):252–260Google Scholar
  44. 44.
    Yang R, Sarkar S, Loeding B (2010) Handling movement epenthesis and hand segmentation ambiguities in continuous sign language recognition using nested dynamic programming. IEEE Trans Pattern Anal Mach Intell 32:462–477Google Scholar
  45. 45.
    Min B-W, Yoon H-S, Soh J, Yang Y-M, Ejima T (1997) Hand gesture recognition using hidden Markov models. In: Systems, Man, and Cybernetics, 1997. Computational cybernetics and simulation. 1997 IEEE international conference, IEEE, pp 4232–4235Google Scholar
  46. 46.
    Bellugi U, Fischer S (1972) A comparison of sign language and spoken language. Cognition 1:173–200Google Scholar
  47. 47.
    Elmezain M, Al-Hamadi A, Appenrodt J, Michaelis B (2009) A hidden Markov model-based isolated and meaningful hand gesture recognition. Int J Electr Comput Syst Eng 3:156–163Google Scholar
  48. 48.
    Grobel K, Assan M (1997) Isolated sign language recognition using hidden Markov models. In: Systems, Man, and Cybernetics, 1997. Computational cybernetics and simulation. 1997 IEEE international conference, IEEE, pp 162–167Google Scholar
  49. 49.
    Lichtenauer JF, Hendriks EA, Reinders MJ (2008) Sign language recognition by combining statistical DTW and independent classification. IEEE Trans Pattern Anal Mach Intell 30:2040–2046Google Scholar
  50. 50.
    Bahlmann C, Burkhardt H (2004) The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping. IEEE Trans Pattern Anal Mach Intell 26:299–310Google Scholar
  51. 51.
    Rekha J, Bhattacharya J, Majumder S (2011) Shape, texture and local movement hand gesture features for indian sign language recognition. In: 3rd international conference on trendz in information sciences and computing (TISC2011), IEEE, pp 30–35Google Scholar
  52. 52.
    Darrell T, Pentland A (1993) Space-time gestures. Comput Vis Pattern Recognit. Proceedings CVPR’93. 1993 IEEE computer society conference, IEEE, pp 335–340Google Scholar
  53. 53.
    Nam Y, Wohn K (1996) Recognition of space-time hand-gestures using hidden Markov model. In: ACM symposium on Virtual reality software and technology, pp 51–58Google Scholar
  54. 54.
    Thomas G (2011) A review of various hand gesture recognition techniques. VSRD Int J Electr Electron Commun Eng 1(7):374–383Google Scholar
  55. 55.
    Ibraheem NA, Khan RZ (2012) Vision based gesture recognition using neural networks approaches: a review. Int J Hum Comput Interact IJHCI 3:1–14Google Scholar
  56. 56.
    Ribeiro HL, Gonzaga A (2006) Hand image segmentation in video sequence by GMM: a comparative analysis. In: 19th Brazilian symposium on computer graphics and image processing, IEEE, pp 357–364Google Scholar
  57. 57.
    Rautaray SS, Agrawal A (2015) Vision based hand gesture recognition for human computer interaction: a survey. Artif Intell Rev 43:1–54Google Scholar
  58. 58.
    Moeslund TB, Granum E (2001) A survey of computer vision-based human motion capture. Comput Vis Image Underst 81:231–268zbMATHGoogle Scholar
  59. 59.
    Moeslund TB, Hilton A, Krüger V (2006) A survey of advances in vision-based human motion capture and analysis. Comput Vis Image Underst 104:90–126Google Scholar
  60. 60.
    Mitra S, Acharya T (2007) Gesture recognition: a survey. IEEE Trans Syst Man Cybern Part C Appl Rev 37:311–324Google Scholar
  61. 61.
    Wu Y, Huang TS (1999) Vision-based gesture recognition: a review. In: International gesture workshop, Springer, pp 103–115Google Scholar
  62. 62.
    Wu Y, Huang TS (1999) Human hand modeling, analysis and animation in the context of HCI. In: Image processing, ICIP 99. Proceedings. 1999 international conference, IEEE, pp 6–10Google Scholar
  63. 63.
    Wang L, Hu W, Tan T (2003) Recent developments in human motion analysis. Pattern Recognit 36:585–601Google Scholar
  64. 64.
    Brand M, Oliver N, Pentland A (1997) Coupled hidden Markov models for complex action recognition. In: Computer vision and pattern recognition, proceedings. 1997 IEEE computer society conference, IEEE, pp 994–999Google Scholar
  65. 65.
    Ghahramani Z, Jordan MI (1997) Factorial hidden Markov models. Mach Learn 29:245–273zbMATHGoogle Scholar
  66. 66.
    Dardas N, Chen Q, Georganas ND, Petriu EM (2010) Hand gesture recognition using bag-of-features and multi-class support vector machine. In: Haptic audio-visual environments and games (HAVE), 2010 IEEE international symposium, IEEE, pp 1–5Google Scholar
  67. 67.
    Pu Q, Gupta S, Gollakota S, Patel S (2013) Whole-home gesture recognition using wireless signals. In: Proceedings of the 19th annual international conference on Mobile computing and networking, ACM, pp 27–38Google Scholar
  68. 68.
    Vogler C, Metaxas D (1998) ASL recognition based on a coupling between HMMs and 3D motion analysis. In: computer vision, 1998. Sixth international conference, IEEE, pp 363–369Google Scholar
  69. 69.
    Karami A, Zanj B, Sarkaleh AK (2011) Persian sign language (PSL) recognition using wavelet transform and neural networks. Expert Syst Appl 38:2661–2667Google Scholar
  70. 70.
    Zaki MM, Shaheen SI (2011) Sign language recognition using a combination of new vision based features. Pattern Recognit Lett 32:572–577Google Scholar
  71. 71.
    Vogler C, Metaxas D (1997) Adapting hidden Markov models for ASL recognition by using three-dimensional computer vision methods. In: Systems, Man, and Cybernetics, Computational cybernetics and simulation. 1997 IEEE international conference, IEEE, pp 156–161Google Scholar
  72. 72.
    Gavrila DM (1999) The visual analysis of human movement: A survey. Comput Vis Image Underst 73:82–98zbMATHGoogle Scholar
  73. 73.
    Zhang X, Chen X, Li Y, Lantz V, Wang K, Yang J (2011) A framework for hand gesture recognition based on accelerometer and EMG sensors. IEEE Trans Syst Man Cybern Part A Syst Hum 41:1064–1076Google Scholar
  74. 74.
    Kainz O, Jakab F (2014) Approach to hand tracking and gesture recognition based on depth-sensing cameras and EMG monitoring. Acta Inf Prag 3:104–112Google Scholar
  75. 75.
    Vyas KK, Pareek A, Tiwari S (2013) Gesture recognition and control. Int J Recent Innov Trends Comput Commun 1(7):575–581Google Scholar
  76. 76.
    Kurdyumov R, Ho P, Ng J (2011) Sign language classification using webcam imagesGoogle Scholar
  77. 77.
    Wong S-F, Cipolla R (2005) Real-time adaptive hand motion recognition using a sparse Bayesian classifier. In: Int Workshop Hum Comput Interact, Springer, pp 170–179Google Scholar
  78. 78.
    Von Agris U, Kraiss KF (2007) Towards a video corpus for signer-independent continuous sign language recognition. Gesture Hum Comput Interact Simul, LisbonGoogle Scholar
  79. 79.
    Zhang H, Wang Y, Deng C (2011) Application of gesture recognition based on simulated annealing BP neural network. In: Electronic and mechanical engineering and information technology (EMEIT), 2011 international conference, IEEE, pp 178–181Google Scholar
  80. 80.
    Molchanov P, Gupta S, Kim K, Kautz J (2015) Hand gesture recognition with 3D convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 1–7Google Scholar
  81. 81.
    Liu N, Lovell BC (2003) Gesture classification using hidden Markov models and viterbi path counting. In: VIIth digital image computing: techniques and applicationsGoogle Scholar
  82. 82.
    Barros PV, Júnior NT, Bisneto JM, Fernandes BJ, Bezerra BL, Fernandes SM (2013) An effective dynamic gesture recognition system based on the feature vector reduction for SURF and LCS. In: International conference on artificial neural networks, Springer, pp 412–419Google Scholar
  83. 83.
    Kumar G, Bhatia PK (2014) A detailed review of feature extraction in image processing systems. In: 2014 fourth international conference on advanced computing and communication technologies, IEEE, pp 5–12Google Scholar
  84. 84.
    Stergiopoulou E, Papamarkos N (2009) Hand gesture recognition using a neural network shape fitting technique. Eng Appl Artif Intell 22:1141–1158Google Scholar
  85. 85.
    Graham J, Starzyk JA (2008) A hybrid self-organizing neural gas based network. In: 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence), IEEE, pp 3806–3813Google Scholar
  86. 86.
    Rybach D, Ney IH, Borchers J, Deselaers D-IT (2006) Appearance-based features for automatic continuous sign language recognition. Master’s thesis, Human Language Technology and Pattern Recognition Group. RWTH Aachen University, AachenGoogle Scholar
  87. 87.
    Hong P, Turk M, Huang TS (2000) Gesture modeling and recognition using finite state machines. In: Automatic face and gesture recognition proceedings. fourth IEEE international conference, IEEE, pp 410–415Google Scholar
  88. 88.
    Bhuyan MK, Ramaraju VV, Iwahori Y (2014) Hand gesture recognition and animation for local hand motions. Int J Mach Learn Cybern 5:607–623Google Scholar
  89. 89.
    Baranwal N, Nandi G (2017) An efficient gesture based humanoid learning using wavelet descriptor and MFCC techniques. Int J Mach Learn Cybern 8(4):1369–1388Google Scholar
  90. 90.
    Bukhari J, Rehman M, Malik SI, Kamboh AM, Salman A (2015) American sign language translation through sensory glove; signspeak. Int J u-e-Serv Sci Technol 8Google Scholar
  91. 91.
    Sethi A, Hemanth S, Kumar K, Bhaskara Rao N, Krishnan R (2012) SignPro—an application suite for deaf and dumb. IJCSET: 1203–1206Google Scholar
  92. 92.
    Abdelnasser H, Youssef M, Harras KA (2015) Wigest: a ubiquitous wifi-based gesture recognition system. In: 2015 IEEE conference on computer communications (INFOCOM, IEEE, pp 1472–1480Google Scholar
  93. 93.
    Wan Q, Li Y, Li C, Pal R (2014) Gesture recognition for smart home applications using portable radar sensors. In: 2014 36th annual international conference of the IEEE engineering in medicine and biology society, IEEE, pp 6414–6417Google Scholar
  94. 94.
    Murakami K, Taguchi H (1991) Gesture recognition using recurrent neural networks. In: Proceedings of the SIGCHI conference on human factors in computing systems, ACM, pp 237–242Google Scholar
  95. 95.
    Mohandes M, Deriche M, Liu J (2014) Image-based and sensor-based approaches to Arabic sign language recognition. IEEE Trans Hum Mach Syst 44:551–557Google Scholar
  96. 96.
    Chuan C-H, Regina E, Guardino C (2014) American Sign Language recognition using leap motion sensor. In: Machine learning and applications (ICMLA), 13th international conference, IEEE, pp 541–544Google Scholar
  97. 97.
    Mohandes M, Aliyu S, Deriche M (2014) Arabic sign language recognition using the leap motion controller. In: 2014 IEEE 23rd international symposium on industrial electronics (ISIE), IEEE, pp 960–965Google Scholar
  98. 98.
    Funasaka M, Ishikawa Y, Takata M, Joe K (2015) Sign language recognition using leap motion controller. In: Proceedings of the international conference on parallel and distributed processing techniques and applications (PDPTA), The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), p 263Google Scholar
  99. 99.
    Potter LE, Araullo J, Carter L (2013) The leap motion controller: a view on sign language. In: Proceedings of the 25th Australian computer–human interaction conference: augmentation, application, innovation, collaboration, ACM, pp 175–178Google Scholar
  100. 100.
    Marin G, Dominio F, Zanuttigh P (2014) Hand gesture recognition with leap motion and Kinect devices. In: 2014 IEEE international conference on image processing (ICIP), IEEE, pp 1565–1569Google Scholar
  101. 101.
    Shukla J, Dwivedi A (2014) A method for hand gesture recognition. In: Communication systems and network technologies (CSNT), 2014 fourth international conference, IEEE, pp. 919–923Google Scholar
  102. 102.
    Maisto M, Panella M, Liparulo L, Proietti A (2013) An accurate algorithm for the identification of fingertips using an RGB-D camera. IEEE J Emerg Sel Top Circuits Syst 3(2):272–83Google Scholar
  103. 103.
    Yeo HS, Lee BG, Lim H (2015) Hand tracking and gesture recognition system for human–computer interaction using low-cost hardware. Multimed Tools Appl 74(8):2687–715.Google Scholar
  104. 104.
    Tofighi G, Monadjemi SA, Ghasem-Aghaee N (2010) Rapid hand posture recognition using adaptive histogram template of skin and hand edge contour. In: 2010 6th Iranian conference on machine vision and image processing, IEEE, pp. 1–5Google Scholar
  105. 105.
    Han G, Choi H (2014) MPEG-U based advanced user interaction interface system using hand posture recognition. In: 16th international conference on advanced communication technology, IEEE, pp. 512–517Google Scholar
  106. 106.
    Keskin C, Kıraç F, Kara YE, Akarun L (2013) Real time hand pose estimation using depth sensors. In: Consumer depth cameras for computer vision 2013, Springer, London, pp 119–137Google Scholar
  107. 107.
    Billiet L, Mogrovejo O, Antonio J, Hoffmann M, Meert W, Antanas L (2013) Rule-based hand posture recognition using qualitative finger configurations acquired with the Kinect. In: Proceedings of the 2nd international conference on pattern recognition applications and methods, pp 1–4Google Scholar
  108. 108.
    Mo Z, Neumann U (2006) Real-time hand pose recognition using low-resolution depth images. CVPR 2:1499–1505Google Scholar
  109. 109.
    Vančo M, Minárik I, Rozinaj G (2012) Gesture identification for system navigation in 3D scene. In: ELMAR, 2012 proceedings, IEEE, pp 45–48Google Scholar
  110. 110.
    Ganapathyraju S (2013) Hand gesture recognition using convexity hull defects to control an industrial robot. In: Instrumentation control and automation (ICA), 2013 3rd international conference, IEEE, pp. 63–67Google Scholar
  111. 111.
    Manresa C, Varona J, Mas R, Perales FJ (2005) Hand tracking and gesture recognition for human–computer interaction. ELCVIA Electron Lett Comput Vis Image Anal 5(3):96–104Google Scholar
  112. 112.
    Lahiani H, Elleuch M, Kherallah M (2015) Real time hand gesture recognition system for android devices. In: Intelligent systems design and applications (ISDA), 2015 15th international conference, IEEE, pp. 591–596Google Scholar
  113. 113.
    Tariq M, Iqbal A, Zahid A, Iqbal Z, Akhtar J (2012) Sign language localization: learning to eliminate language dialects. In: Multitopic conference (INMIC), 2012 15th international, IEEE, pp 17–22Google Scholar
  114. 114.
    Pedersoli F, Benini S, Adami N, Leonardi R (2014) XKin: an open source framework for hand pose and gesture recognition using kinect. Vis Comput 30(10):1107–1122Google Scholar
  115. 115.
    Shaik KB, Ganesan P, Kalist V, Sathish BS, Jenitha JM (2015) Comparative study of skin color detection and segmentation in HSV and YCbCr color space. Procedia Comput Sci 57:41–48Google Scholar
  116. 116.
    Kaur A, Kranthi BV (2012) Comparison between YCbCr color space and CIELab color space for skin color segmentation. IJAIS 3(4):30–3Google Scholar
  117. 117.
    Tsagaris A, Manitsaris S (2013) Colour space comparison for skin detection in finger gesture recognition. Int J Adv Eng Technol 6(4):1431Google Scholar
  118. 118.
    Qiu-yu Z, Jun-chi L, Mo-yi Z, Hong-xiang D, Lu L (2015) Hand gesture segmentation method based on YCbCr color space and K-means clustering. Interaction 8:106–16Google Scholar
  119. 119.
    Kaur G, Kaur P. Face recognition using YCbCr and CIElab skin color segmentation methods: a reviewGoogle Scholar
  120. 120.
    Sun HM (2010) Skin detection for single images using dynamic skin color modeling. Pattern Recognit 43(4):1413–1420Google Scholar
  121. 121.
    Zahedi M, Gorgan I (2007) Robust appearance based sign language recognition, Doctoral dissertation. RWTH Aachen UniversityGoogle Scholar
  122. 122.
    Dreuw P, Forster J, Ney H (2010) Tracking benchmark databases for video-based sign language recognition. In: European conference on computer vision, Springer, Berlin, pp 286–297Google Scholar
  123. 123.
    Dreuw P, Stein D, Ney H (2007) Enhancing a sign language translation system with vision-based features. In: International gesture workshop, Springer, Berlin, pp 108–113Google Scholar
  124. 124.
    Kak AC (2002) Purdue RVL-SLLL ASL database for automatic recognition of American sign language. In: Proceedings of the 4th IEEE international conference on multimodal interfaces, IEEE Computer Society, pp. 167Google Scholar
  125. 125.
    Forster J, Schmidt C, Hoyoux T, Koller O, Zelle U, Piater JH, Ney H (2012) RWTH-PHOENIX-weather: a large vocabulary sign language recognition and translation corpus. In: LREC, pp. 3785–3789Google Scholar
  126. 126.
    Dreuw P, Rybach D, Deselaers T, Zahedi M, Ney H (2007) Speech recognition techniques for a sign language recognition system. Hand 60:80Google Scholar
  127. 127.
    Bungeroth J, Stein D, Dreuw P, Ney H, Morrissey S, Way A, van Zijl L (2008) The ATIS sign language corpusGoogle Scholar
  128. 128.
    Dreuw P, Neidle C, Athitsos V, Sclaroff S, Ney H (2008) Benchmark databases for video-based automatic sign language recognition. LRECGoogle Scholar
  129. 129.
    Stein D, Dreuw P, Ney H, Morrissey S, Way A (2007) Hand in hand: automatic sign language to English translationGoogle Scholar
  130. 130.
    Zahedi M, Keysers D, Ney H (2005) Pronunciation clustering and modeling of variability for appearance-based sign language recognition. In: International gesture workshop, Springer, Berlin, pp. 68–79Google Scholar
  131. 131.
    Yasir R, Khan RA (2014) Two-handed hand gesture recognition for Bangla sign language using LDA and ANN. In: Software, knowledge, information management and applications (SKIMA), 2014 8th international conference, IEEE, pp 1–5Google Scholar
  132. 132.
    Suriya M, Sathyapriya N, Srinithi M, Yesodha V (2016) Survey on real time sign language recognition system: an LDA approach. In: International conference on exploration and innovations in engineering and technology, ICEIET, pp. 219–225Google Scholar
  133. 133.
    Nummiaro K, Koller-Meier E, Van Gool L (2003) An adaptive color-based particle filter. Image Vis Comput 21(1):99–110zbMATHGoogle Scholar
  134. 134.
    Shan C, Wei Y, Tan T, Ojardias F (2004) Real time hand tracking by combining particle filtering and mean shift. In: Automatic face and gesture recognition, 2004. Proceedings. Sixth IEEE international conference, IEEE, pp. 669–674Google Scholar
  135. 135.
    Bretzner L, Laptev I, Lindeberg T (2002) Hand gesture recognition using multi-scale colour features, hierarchical models and particle filtering. In: Automatic face and gesture recognition, 2002. Proceedings. Fifth IEEE international conference, IEEE, pp. 423–428Google Scholar
  136. 136.
    Kakumanu P, Makrogiannis S, Bourbakis N (2007) A survey of skin-color modeling and detection methods. Pattern Recognit 40(3):1106–1122zbMATHGoogle Scholar
  137. 137.
    Li P, Zhang T, Pece AE (2003) Visual contour tracking based on particle filters. Image Vis Comput 21(1):111–123Google Scholar
  138. 138.
    Czyz J, Ristic B, Macq B (2007) A particle filter for joint detection and tracking of color objects. Image Vis Comput 25(8):1271–1281Google Scholar
  139. 139.
    Shan C, Tan T, Wei Y (2007) Real-time hand tracking using a mean shift embedded particle filter. Pattern Recognit 40(7):1958–1970zbMATHGoogle Scholar
  140. 140.
    Naik GR, Acharyya A, Nguyen HT (2014) Classification of finger extension and flexion of EMG and Cyberglove data with modified ICA weight matrix. In: 2014 36th annual international conference of the IEEE engineering in medicine and biology society, IEEE, pp. 3829–3832Google Scholar
  141. 141.
    Huong TN, Huu TV, Le Xuan T (2015) Static hand gesture recognition for Vietnamese sign language (VSL) using principle components analysis. In: 2015 International conference on communications, management and telecommunications (ComManTel), IEEE, pp. 138–141Google Scholar
  142. 142.
    Jasim M, Hasanuzzaman M (2014) Sign language interpretation using linear discriminant analysis and local binary patterns. In: Informatics, electronics and vision (ICIEV), 2014 international conference, IEEE, pp 1–5Google Scholar
  143. 143.
    Abhishek KS, Qubeley LC, Ho D (2016) Glove-based hand gesture recognition sign language translator using capacitive touch sensor. In: Electron devices and solid-state circuits (EDSSC), 2016 IEEE international conference, IEEE, pp 334–337Google Scholar
  144. 144.
    Sykora P, Kamencay P, Hudec R (2014) Comparison of SIFT and SURF methods for use on hand gesture recognition based on depth map. AASRI Procedia 9:19–24Google Scholar
  145. 145.
    Hartanto R, Susanto A, Santosa PI (2014) Real time static hand gesture recognition system prototype for Indonesian sign language. In: Information technology and electrical engineering (ICITEE), 2014 6th international conference, IEEE, pp 1–6Google Scholar
  146. 146.
    Gupta B, Shukla P, Mittal A (2016) K-nearest correlated neighbor classification for Indian sign language gesture recognition using feature fusion. In: 2016 international conference on computer communication and informatics (ICCCI), IEEE, pp 1–5Google Scholar
  147. 147.
    Bastos IL, Angelo MF, Loula AC (2015) Recognition of Static Gestures applied to Brazilian Sign Language (Libras). In: 2015 28th SIBGRAPI conference on graphics, patterns and images, IEEE, pp 305–312Google Scholar
  148. 148.
    Ding L, Martinez AM (2009) Modelling and recognition of the linguistic components in American sign language. Image Vis Comput 27(12):1826–1844Google Scholar
  149. 149.
    Pan TY, Lo LY, Yeh CW, Li JW, Liu HT, Hu MC (2016) Real-time sign language recognition in complex background scene based on a hierarchical clustering classification method. In: Multimedia big data (BigMM), 2016 IEEE second international conference, IEEE, pp 64–67Google Scholar
  150. 150.
    Gabriel J, Marcelo J, Figueiredo LS, Teichrieb V (2016) Evaluating sign language recognition using the Myo Armband. In: Virtual and augmented reality (SVR), 2016 XVIII symposium, IEEE, pp 64–70Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Faculty of Electrical EngineeringUniversiti Teknologi MalaysiaSkudaiMalaysia
  2. 2.School of EngineeringMonash University MalaysiaSubang JayaMalaysia

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