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Hand Gesture Analysis

  • Chapter
Computer Analysis of Human Behavior

Abstract

This chapter presents a thorough overview of automatic hand gesture analysis. We cover all aspects of hand gesture recognition from detection of the hand to modeling of gestures. Based on a general gesture analysis framework, we present and discuss each necessary building block that is required to design a complete system. The last section presents two example applications: A sign language tutor for isolated signs and a gesture tracking and recognition system for continuous gestures and signs.

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References

  1. Aran, O., Ari, I., Benoit, A., Campr, P., Carrillo, A.H., Fanard, F.-X., Akarun, L., Caplier, A., Sankur, B.: Signtutor: An interactive system for sign language tutoring. IEEE Multimed. 16(1), 81–93 (2009)

    Article  Google Scholar 

  2. Aran, O., Burger, T., Caplier, A., Akarun, L.: A belief-based sequential fusion approach for fusing manual and non-manual signs. Pattern Recognit. 42(5), 812–822 (2009)

    Article  MATH  Google Scholar 

  3. Aubert, G., Barlaud, M., Faugeras, O., Jehan-Besson, S.: Image segmentation using active contours: Calculus of variations or shape gradients. SIAM J. Appl. Math. 63(6), 2128–2154 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  4. Bengio, Y., Frasconi, P.: Input-output HMM’s for sequence processing. IEEE Trans. Neural Netw. 7(5), 1231–1249 (1996)

    Article  Google Scholar 

  5. Black, M.J., Jepson, A.D.: Eigentracking: Robust matching and tracking of articulated objects using a view-based representation. In: Proceedings of the 4th European Conference on Computer Vision (ECCV ’96), vol. I, pp. 329–342. Springer, London (1996)

    Google Scholar 

  6. Bobick, A., Davis, J.: Real-time recognition of activity using temporal templates. In: Proceedings of the Workshop on Applications of Computer Vision (1996)

    Google Scholar 

  7. Clarke, T.A., Fryer, J.G.: The development of camera calibration methods and models. Photogramm. Rec. 16(91), 51–66 (1998)

    Article  Google Scholar 

  8. Cornett, R.O.: Cued speech. Am. Ann. Deaf 112, 3–13 (1967)

    Google Scholar 

  9. Cui, Y., Swets, D.L., Weng, J.J.: Learning-based hand sign recognition using shoslif-m. In: Proceedings of the Fifth International Conference on Computer Vision, ICCV ’95, p. 631, Washington, DC, USA. IEEE Comput. Soc., Los Alamitos (1995)

    Google Scholar 

  10. Doucet, A., De Freitas, N., Gordon, N. (eds.): Sequential Monte Carlo Methods in Practice. Springer, Berlin (2001)

    MATH  Google Scholar 

  11. Flusser, J., Suk, T.: Rotation moment invariants for recognition of symmetric objects. IEEE Trans. Image Process. 15, 3784–3790 (2006)

    Article  MathSciNet  Google Scholar 

  12. Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory IT-8, 179–187 (1962)

    Google Scholar 

  13. Hu, W., Tieniu, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. Syst. Man Cybern. 34, 334–352 (2004)

    Google Scholar 

  14. Isard, M., Blake, A.: Condensation—conditional density propagation for visual tracking. Int. J. Comput. Vis. 26(1), 5–28 (1998)

    Article  Google Scholar 

  15. Just, A., Bernier, O., Marcel, S.: HMM and IOHMM for the recognition of mono- and bi-manual 3D hand gestures. IDIAP-RR 39, IDIAP, 2004

    Google Scholar 

  16. Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82, 35–45 (1960)

    Google Scholar 

  17. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)

    Article  Google Scholar 

  18. Kendon, A.: Current issues in the study of gesture. In: Nespoulous, J.L., Peron, P., Lecours, A.R. (eds.) The Biological Foundations of Gestures: Motor and Semiotic Aspects, pp. 23–47. Erlbaum, Hillsdale (1986)

    Google Scholar 

  19. Kendon, A.: Gesture. Cambridge (2004)

    Google Scholar 

  20. Keskin, C., Akarun, L.: STARS: Sign tracking and recognition system using input–output HMMs. Pattern Recognit. Lett. 30, 1086–1095 (2009)

    Article  Google Scholar 

  21. Khotanzad, A., Hong, Y.H.: Invariant image recognition by Zernike moments. IEEE Trans. Pattern Anal. Mach. Intell. 12, 489–497 (1990)

    Article  Google Scholar 

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

    Article  Google Scholar 

  23. Marcel, S., Bernier, O., Viallet, J.-E., Collobert, D.: Hand gesture recognition using input-output hidden Markov models. In: FG ’00: Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2008, p. 456, Washington, DC, USA. IEEE Comput. Soc., Los Alamitos (2000)

    Chapter  Google Scholar 

  24. McNeill, D., Levy, E.: Conceptual representations in language activity and gesture. In: Jarvella, R., Klein, W. (eds.) Speech, Place, and Action. Wiley, New York (1982)

    Google Scholar 

  25. Mish, F.C.: The Merriam-Webster Dictionary. Merriam-Webster, Chicago (1997)

    Google Scholar 

  26. Mitiche, A., Sekkati, H.: Optical flow 3d segmentation and interpretation: A variational method with active curve evolution and level sets. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1818–1829 (2006)

    Article  Google Scholar 

  27. Morency, L.-P., Quattoni, A., Darrell, T.: Latent-dynamic discriminative models for continuous gesture recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2007)

    Google Scholar 

  28. Oka, K., Sato, Y., Koike, H.: Real-time tracking of multiple fingertips and gesture recognition for augmented desk interface systems. In: Proceedings of Fifth IEEE International Conference on Automatic Face and Gesture Recognition, Washington D.C., US, p. 429 (2002)

    Chapter  Google Scholar 

  29. Pavlovic, V., Sharma, R., Huang, T.S.: Visual interpretation of hand gestures for human-computer interaction: A review. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 677–695 (1997)

    Article  Google Scholar 

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

    Article  Google Scholar 

  31. Riviere, J., Guitton, P.: Real time model based tracking using silhouette features. In: Proceedings of RFIA, Toulouse, France (2004)

    Google Scholar 

  32. Stokoe, W.C.: Sign language structure: An outline of the visual communication systems of the American deaf. Stud. Linguist., Occas. Pap. 8 (1960)

    Google Scholar 

  33. Triesch, J., von der Malsburg, C.: Classification of hand postures against complex backgrounds using elastic graph matching. Image Vis. Comput. 20(13–14), 937–943 (2002)

    Article  Google Scholar 

  34. Ueda, E., Matsumoto, Y., Imai, M., Ogasawara, T.: A hand-pose estimation for vision-based human interfaces. IEEE Trans. Ind. Electron. 50(4), 676–684 (2003)

    Article  Google Scholar 

  35. Wang, S.B., Quattoni, A., Morency, L.-P., Demirdjian, D.: Hidden conditional random fields for gesture recognition. In: CVPR ’06: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, pp. 1521–1527. IEEE Comput. Soc., Los Alamitos (2006)

    Google Scholar 

  36. Wu, Y., Huang, T.S.: Hand modeling, analysis, and recognition for vision based human computer interaction. IEEE Signal Process. Mag. 21(1), 51–60 (2001)

    Google Scholar 

  37. Yang, H.-D., Sclaroff, S., Lee, S.-W.: Sign language spotting with a threshold model based on conditional random fields. IEEE Trans. Pattern Anal. Mach. Intell. 31, 1264–1277 (2009)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Engineering Department, Boğaziçi University, Istanbul, Turkey

    Cem Keskin & Lale Akarun

  2. Idiap Research Institute, Martigny, Switzerland

    Oya Aran

Authors
  1. Cem Keskin
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  2. Oya Aran
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  3. Lale Akarun
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Corresponding author

Correspondence to Cem Keskin .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Boğaziçi University, Bebek, 34342, Istanbul, Turkey

    Albert Ali Salah

  2. Informatics Institute, University of Amsterdam, Science Park 904, Amsterdam, 1098 XH, Netherlands

    Theo Gevers

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© 2011 Springer-Verlag London Limited

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Keskin, C., Aran, O., Akarun, L. (2011). Hand Gesture Analysis. In: Salah, A., Gevers, T. (eds) Computer Analysis of Human Behavior. Springer, London. https://doi.org/10.1007/978-0-85729-994-9_6

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  • DOI: https://doi.org/10.1007/978-0-85729-994-9_6

  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-993-2

  • Online ISBN: 978-0-85729-994-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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