Skip to main content
SpringerLink
Log in

Introduction

  • Chapter
  • First Online:
Book cover Gesture Recognition

Part of the book series: Studies in Computational Intelligence ((SCI,volume 724))

  • 1039 Accesses

Abstract

The chapter provides an introduction to gesture recognition. It begins with a thorough review of the principles of gesture recognition undertaken in the existing works. The chapter then elaborately introduces the Kinect sensor, which has recently emerged as an important machinery to capture human-gestures. Various tools and techniques relevant to image processing, pattern recognition and computational intelligence, which have necessary applications in gesture recognition, are also briefly explained here. The chapter outlines possible applications of gesture recognition. The scope of the book is also appended at the end of the chapter.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. C. Darwin, The Expression of the Emotions in Man and Animals, vol 526 (University of Chicago Press, 1965)

    Google Scholar 

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

    Google Scholar 

  3. T.S. Chow, Testing software design modeled by finite-state machines. IEEE Trans. Softw. Eng. 3, 178–187 (1978)

    Article  MATH  Google Scholar 

  4. L. Rabiner, B. Juang, An introduction to hidden Markov models. IEEE ASSP Mag. 3(1), 4–16 (1986)

    Google Scholar 

  5. M.S. Arulampalam, S. Maskell, N. Gordon, T. Clapp, A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process. 50(2), 174–188 (2002)

    Article  Google Scholar 

  6. P. Hong, M. Turk, T. S. Huang, Gesture modeling and recognition using finite state machines, in Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition (2000), pp. 410–415

    Google Scholar 

  7. M. Brand, N. Oliver, A. Pentland, Coupled hidden Markov models for complex action recognition, in Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision And Pattern Recognition (1997), pp. 994–999

    Google Scholar 

  8. J.G. Kemeny, J.L. Snell, Finite Markov Chains, vol. 356 (van Nostrand, Princeton, 1960)

    MATH  Google Scholar 

  9. M.S. Grewal, Kalman Filtering (Springer, 2011)

    Google Scholar 

  10. N.R. Draper, H. Smith, E. Pownell, Applied Regression Analysis, vol. 3 (Wiley, New York, 1966)

    Google Scholar 

  11. R.C. Littell, Sas (Wiley Online Library, 2006)

    Google Scholar 

  12. E.A. Wan, R. Van Der Merwe, The unscented Kalman filter for nonlinear estimation, in Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000. AS-SPCC. The IEEE 2000 (2000), pp. 153–158

    Google Scholar 

  13. S. Theodoridis, A. Pikrakis, K. Koutroumbas, D. Cavouras, Introduction to Pattern Recognition: A Matlab Approach. (Academic Press, 2010)

    Google Scholar 

  14. R.O. Duda, P.E. Hart, Pattern Classification and Scene Analysis, vol. 3 (Wiley, New York, 1973)

    MATH  Google Scholar 

  15. J.A.K. Suykens, J. Vandewalle, Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)

    Article  MATH  Google Scholar 

  16. D. Bhattacharya, A. Konar, P. Das, Secondary factor induced stock index time-series prediction using Self-Adaptive Interval Type-2 Fuzzy Sets. Neurocomputing 171, 551–568 (2016)

    Article  Google Scholar 

  17. P. Rakshit, A. Konar, S. Das, L.C. Jain, A.K. Nagar, Uncertainty management in differential evolution induced multiobjective optimization in presence of measurement noise. IEEE Trans. Syst. Man Cybern. Syst. 44(7), 922–937 (2014)

    Article  Google Scholar 

  18. L.A. Zadeh, Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  MATH  Google Scholar 

  19. H.-J. Zimmermann, Fuzzy control, in Fuzzy Set Theory—and Its Applications (Springer, 1996), pp. 203–240

    Google Scholar 

  20. J.M. Mendel, D. Wu, Perceptual reasoning for perceptual computing. IEEE Trans. Fuzzy Syst. 16(6), 1550–1564 (2008)

    Article  Google Scholar 

  21. Q. Liang, J.M. Mendel, Interval type-2 fuzzy logic systems: theory and design. IEEE Trans. Fuzzy Syst. 8(5), 535–550 (2000)

    Article  Google Scholar 

  22. C. Wagner, H. Hagras, Toward general type-2 fuzzy logic systems based on zSlices. IEEE Trans. Fuzzy Syst. 18(4), 637–660 (2010)

    Article  Google Scholar 

  23. K.T. Atanassov, Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20(1), 87–96 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  24. L.A.Z. Adeh, Calculus of fuzzy restrictions. Fuzzy Sets, Fuzzy Logic, Fuzzy Syst. Sel. Pap. by Lotfi A Zadeh 6, 210 (1996)

    Article  Google Scholar 

  25. A.T.C. Goh, Back-propagation neural networks for modeling complex systems. Artif. Intell. Eng. 9(3), 143–151 (1995)

    Article  Google Scholar 

  26. L. Yingwei, N. Sundararajan, P. Saratchandran, A sequential learning scheme for function approximation using minimal radial basis function neural networks. Neural Comput. 9(2), 461–478 (1997)

    Article  MATH  Google Scholar 

  27. D.F. Specht, Probabilistic neural networks. Neural Networks 3(1), 109–118 (1990)

    Article  Google Scholar 

  28. S.E. Fahlman, C. Lebiere, The cascade-correlation learning architecture, in Advances in neural information processing systems (1990), pp. 524–532

    Google Scholar 

  29. E. Zitzler, L. Thiele, Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)

    Article  Google Scholar 

  30. R. Storn, K. Price, Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  31. P.W. Barone, S. Baik, D.A. Heller, M.S. Strano, Near-infrared optical sensors based on single-walled carbon nanotubes. Nat. Mater. 4(1), 86–92 (2005)

    Article  Google Scholar 

  32. Z. Zhang, Microsoft kinect sensor and its effect. IEEE Multimedia 19(2), 4–10 (2012)

    Article  Google Scholar 

  33. D.M. Gavrila, The visual analysis of human movement: a survey. Comput. Vis. Image Underst. 73(1), 82–98 (1999)

    Article  MATH  Google Scholar 

  34. S. Saha, S. Bhattacharya, A. Konar, Comparison between type-1 fuzzy membership functions for sign language applications, in 2016 International Conference on Microelectronics, Computing and Communications (MicroCom) (2016), pp. 1–6

    Google Scholar 

  35. R. Polana, R. Nelson, Low level recognition of human motion (or how to get your man without finding his body parts), in Proceedings of the 1994 IEEE Workshop on Motion of Non-Rigid and Articulated Objects (1994), pp. 77–82

    Google Scholar 

  36. J. Yamato, J. Ohya, K. Ishii, Recognizing human action in time-sequential images using hidden markov model, in Proceedings of the 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR’92 (1992), pp. 379–385

    Google Scholar 

  37. K. Takahashi, S. Seki, E. Kojima, and R. Oka, “Recognition of dexterous manipulations from time-varying images,” in Proceedings of the 1994 IEEE Workshop on Motion of Non-Rigid and Articulated Objects (1994), pp. 23–28

    Google Scholar 

  38. W.T. Freeman, M. Roth, Orientation histograms for hand gesture recognition, in International Workshop on Automatic Face and Gesture Recognition, vol 12 (1995), pp. 296–301

    Google Scholar 

  39. D. Hariharan, T. Acharya, S. Mitra, Recognizing hand gestures of a dancer, in Pattern Recognition and Machine Intelligence (Springer, 2011), pp. 186–192

    Google Scholar 

  40. E. Hunter, J. Schlenzig, R. Jain, Posture estimation in reduced-model gesture input systems, in International Workshop on Automatic Face-and Gesture-Recognition (1995), pp. 290–295

    Google Scholar 

  41. F.K.H. Quek, Eyes in the interface. Image Vis. Comput. 13(6), 511–525 (1995)

    Article  Google Scholar 

  42. T.F. Cootes, C.J. Taylor, D.H. Cooper, J. Graham, Active shape models-their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995)

    Article  Google Scholar 

  43. I. Jolliffe, Principal Component Analysis (Wiley Online Library, 2002)

    Google Scholar 

  44. S.S. Fels, G.E. Hinton, Glove-talk: a neural network interface between a data-glove and a speech synthesizer. IEEE Trans. Neural Networks 4(1), 2–8 (1993)

    Article  Google Scholar 

  45. A.F. Bobick, A.D. Wilson, A state-based approach to the representation and recognition of gesture. IEEE Trans. Pattern Anal. Mach. Intell. 19(12), 1325–1337 (1997)

    Article  Google Scholar 

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

    Article  Google Scholar 

  47. M.J. Black, A.D. Jepson, A probabilistic framework for matching temporal trajectories: condensation-based recognition of gestures and expressions, in European Conference on Computer Vision (1998), pp. 909–924

    Google Scholar 

  48. C.-C. Lien, C.-L. Huang, Model-based articulated hand motion tracking for gesture recognition. Image Vis. Comput. 16(2), 121–134 (1998)

    Article  Google Scholar 

  49. H.-K. Lee, J.-H. Kim, An HMM-based threshold model approach for gesture recognition. IEEE Trans. Pattern Anal. Mach. Intell. 21(10), 961–973 (1999)

    Article  Google Scholar 

  50. H.-S. Yoon, J. Soh, Y.J. Bae, H.S. Yang, Hand gesture recognition using combined features of location, angle and velocity. Pattern Recognit. 34(7), 1491–1501 (2001)

    Article  MATH  Google Scholar 

  51. C. Vogler, D. Metaxas, A framework for recognizing the simultaneous aspects of american sign language. Comput. Vis. Image Underst. 81(3), 358–384 (2001)

    Article  MATH  Google Scholar 

  52. M.-H. Yang, N. Ahuja, M. Tabb, Extraction of 2d motion trajectories and its application to hand gesture recognition. IEEE Trans. Pattern Anal. Mach. Intell. 24(8), 1061–1074 (2002)

    Article  Google Scholar 

  53. C.W. Ng, S. Ranganath, Real-time gesture recognition system and application. Image Vis. Comput. 20(13), 993–1007 (2002)

    Article  Google Scholar 

  54. L. Bretzner, I. Laptev, T. Lindeberg, Hand gesture recognition using multi-scale colour features, hierarchical models and particle filtering, in Proceedings of the 5th IEEE International Conference on Automatic Face and Gesture Recognition (2002), pp. 423–428

    Google Scholar 

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

    Article  Google Scholar 

  56. Q. Chen, N.D. Georganas, E.M. Petriu, Hand gesture recognition using haar-like features and a stochastic context-free grammar. IEEE Trans. Instrum. Meas. 57(8), 1562–1571 (2008)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  60. J. Liu, L. Zhong, J. Wickramasuriya, V. Vasudevan, uWave: accelerometer-based personalized gesture recognition and its applications. Pervasive Mob. Comput. 5(6), 657–675 (2009)

    Article  Google Scholar 

  61. X. Zhang, X. Chen, Y. Li, V. Lantz, K. Wang, J. Yang, 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 (2011)

    Google Scholar 

  62. R. Xu, S. Zhou, W.J. Li, MEMS accelerometer based nonspecific-user hand gesture recognition. IEEE Sens. J. 12(5), 1166–1173 (2012)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  65. S. Kurakake, R. Nevatia, Description and tracking of moving articulated objects, in Proceedings of the 11th IAPR International Conference on Pattern Recognition, 1992. Vol. I. Conference A: Computer Vision and Applications (1992), pp. 491–495

    Google Scholar 

  66. R.E. Kahn, M.J. Swain, P.N. Prokopowicz, R.J. Firby, Gesture recognition using the perseus architecture, in Proceedings of the 1996 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR’96 (1996), pp. 734–741

    Google Scholar 

  67. W.T. Freeman, K. Tanaka, J. Ohta, K. Kyuma, Computer vision for computer games, in Proceedings of the Second International Conference on Automatic Face and Gesture Recognition (1996), pp. 100–105

    Google Scholar 

  68. Q. Cai, J.K. Aggarwal, Tracking human motion using multiple cameras, in Proceedings of the 13th International Conference on Pattern Recognition, vol 3 (1996), pp. 68–72

    Google Scholar 

  69. A.M. Baumberg, D.C. Hogg, An efficient method for contour tracking using active shape models, in Proceedings of the 1994 IEEE Workshop on Motion of Non-Rigid and Articulated Objects (1994), pp. 194–199

    Google Scholar 

  70. A. Blake, R. Curwen, A. Zisserman, A framework for spatiotemporal control in the tracking of visual contours. Int. J. Comput. Vis. 11(2), 127–145 (1993)

    Article  Google Scholar 

  71. M. Oren, C. Papageorgiou, P. Sinha, E. Osuna, T. Poggio, Pedestrian detection using wavelet templates, in Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (1997), pp. 193–199

    Google Scholar 

  72. B. Heisele, U. Kressel, W. Ritter, Tracking non-rigid, moving objects based on color cluster flow, in Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (1997), pp. 257–260

    Google Scholar 

  73. D.D. Hoffman, B.E. Flinchbaugh, The interpretation of biological motion. Biol. Cybern. 42(3), 195–204 (1982)

    MATH  Google Scholar 

  74. Z. Chen, H.-J. Lee, Knowledge-guided visual perception of 3-D human gait from a single image sequence. IEEE Trans. Syst. Man. Cybern. 22(2), 336–342 (1992)

    Article  Google Scholar 

  75. A. Azarbayejani and A. Pentland, “Real-time self-calibrating stereo person tracking using 3-D shape estimation from blob features,” in Proceedings of the 13th International Conference on Pattern Recognition, vol 3 (1996), pp. 627–632

    Google Scholar 

  76. L.W. Campbell, A.F. Bobick, Recognition of human body motion using phase space constraints, in Proceedings of the 5th International Conference on Computer Vision (1995), pp. 624–630

    Google Scholar 

  77. H. Sidenbladh, M.J. Black, D.J. Fleet, Stochastic tracking of 3D human figures using 2D image motion, in European Conference on Computer Vision (2000), pp. 702–718

    Google Scholar 

  78. H. Kang, C.W. Lee, K. Jung, Recognition-based gesture spotting in video games. Pattern Recognit. Lett. 25(15), 1701–1714 (2004)

    Article  Google Scholar 

  79. G. Rigoll, A. Kosmala, S. Eickeler, High performance real-time gesture recognition using hidden markov models, in International Gesture Workshop (1997), pp. 69–80

    Google Scholar 

  80. A.D. Wilson, A.F. Bobick, Parametric hidden markov models for gesture recognition. IEEE Trans. Pattern Anal. Mach. Intell. 21(9), 884–900 (1999)

    Article  Google Scholar 

  81. A. Elgammal, V. Shet, Y. Yacoob, L.S. Davis, Learning dynamics for exemplar-based gesture recognition, in Computer Vision and Pattern Recognition, vol 1 (2003), p. I

    Google Scholar 

  82. S.B. Wang, A. Quattoni, L.-P. Morency, D. Demirdjian, T. Darrell, Hidden conditional random fields for gesture recognition, in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol 2 (2006), pp. 1521–1527

    Google Scholar 

  83. H.-D. Yang, A.-Y. Park, S.-W. Lee, Gesture spotting and recognition for human–robot interaction. IEEE Trans. Robot. 23(2), 256–270 (2007)

    Article  Google Scholar 

  84. L.-P. Morency, A. Quattoni, T. Darrell, “Latent-dynamic discriminative models for continuous gesture recognition,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR’07 (2007), pp. 1–8

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Artificial Intelligence Laboratory, Department of Electronics and Tele-Communication Engineering, Jadavpur University, Kolkata, India

    Amit Konar

  2. Artificial Intelligence Laboratory, Department of Electronics and Tele-Communication Engineering, Jadavpur University, Kolkata, India

    Sriparna Saha

Authors
  1. Amit Konar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sriparna Saha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amit Konar .

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Konar, A., Saha, S. (2018). Introduction. In: Gesture Recognition. Studies in Computational Intelligence, vol 724. Springer, Cham. https://doi.org/10.1007/978-3-319-62212-5_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-62212-5_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62210-1

  • Online ISBN: 978-3-319-62212-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation