http://iet.metastore.ingenta.com
1887

Probabilistic gait modelling and recognition

Probabilistic gait modelling and recognition

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Computer Vision — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Biometric researchers have recently found considerable applicability of gait recognition in visual surveillance systems. This study proposes a probabilistic framework for gait modelling that is applied to gait recognition. The basic idea of this framework is to consider the silhouette shape as a multivariate random variable and model it in a full probabilistic framework. The Bernoulli mixture model is employed to model silhouette distribution and recursive algorithms are provided for silhouette image and sequence classification. Finally, the proposed probabilistic method is applied to benchmark databases and its validity is demonstrated through experiments.

References

    1. 1)
      • 1. Li, X.L., Maybank, S.J., Yan, S.C., Tao, D.C., Xu, D.: ‘Gait components and their application to gender recognition’, IEEE Trans. Syst. Man Cybern. C, Appl. Rev., 2008, 38, (2), pp. 145155.
    2. 2)
      • 2. Huang, X.X., Boulgouris, N.V.: ‘Human gait recognition based on multiview gait sequences’, EURASIP J. Adv. Signal Process., 2008, Article ID 629102, pp. 18.
    3. 3)
      • 3. Boulgouris, N.V., Hatzinakos, D., Plataniotis, K.N.: ‘Gait recognition: a challenging signal processing technology for biometric identification’, IEEE Signal Process. Mag., 2005, 22, (6), pp. 7890.
    4. 4)
      • 4. Johnson, A.Y., Bobick, A.F.: ‘A multi-view method for gait recognition using static body parameters’. Proc. Int. Conf. on Audio- and Video-Based Biometric Person Authentication, Halamstad, Sweden, June 2001, pp. 301311.
    5. 5)
      • 5. Lee, L., Grimson, W.E.L.: ‘Gait analysis for recognition and classification’. Proc. IEEE Int. Conf. on Automatic Face and Gesture Recognition, Washington, DC, USA, May 2002, pp. 155162.
    6. 6)
      • 6. Cunado, D., Nixon, M.S., Carter, J.N.: ‘Automatic extraction and description of human gait models for recognition purposes’, Comput. Vis. Image Underst., 2003, 90, (1), pp. 141.
    7. 7)
      • 7. Wagg, D.K., Nixon, M.S.: ‘On automated model-based extraction and analysis of gait’. Proc. IEEE Int. Conf. on Automatic Face and Gesture Recognition, Seoul, Korea, May 2004, pp. 1116.
    8. 8)
      • 8. Zhou, Z., Prugel-Bennett, A., Damper, R.I.: ‘A Bayesian framework for extracting human gait using strong prior knowledge’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (11), pp. 17381752.
    9. 9)
      • 9. Kim, D., Kim, D., Paik, J.: ‘Gait recognition using active shape model and motion prediction’, IET Comput. Vis., 2010, 4, (1), pp. 2536.
    10. 10)
      • 10. Liu, Y.X., Collins, R., Tsin, Y.H.: ‘Gait sequence analysis using frieze patterns’. Proc. European Conf. on Computer Vision, Copenhagen, Denmark, May 2002, pp. 657671.
    11. 11)
      • 11. Kale, A., Cuntoor, N., Yegnanarayana, B., Rajagopalan, A.N., Chellappa, R.: ‘Gait analysis for human identification’. Proc. Int. Conf. on Audio- and Video-Based Biometric Person Authentication, Guilford, UK, June 2003, pp. 706714.
    12. 12)
      • 12. Wang, L., Tan, T.N., Hu, W.M., Ning, H.Z.: ‘Silhouette analysis-based gait recognition for human identification’, IEEE Trans. Pattern Anal. Mach. Intell., 2003, 25, (12), pp. 15051518.
    13. 13)
      • 13. Han, J., Bhanu, B.: ‘Individual recognition using gait energy image’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (2), pp. 316322.
    14. 14)
      • 14. Drosou, A., Stavropoulos, G., Ioannidis, D., Moustakas, K., Tzovaras, D.: ‘Unobtrusive multi-modal biometric recognition using activity-related signatures’, IET Comput. Vis., 2011, 5, (6), pp. 367379.
    15. 15)
      • 15. Lam, T.H.W., Lee, R.S.T.: ‘A new representation for human gait recognition: motion silhouettes image (MSI)’. Proc. Int. Conf. Advances in Biometrics, Hong Kong, China, January 2006, pp. 612618.
    16. 16)
      • 16. Nizami, I.F., Hong, S., Lee, H., Lee, B., Kim, E.: ‘Automatic gait recognition based on probabilistic approach’, Int. J. Imaging Syst. Technol., 2010, 20, (4), pp. 400408.
    17. 17)
      • 17. Bazin, A.I., Nixon, M.S.: ‘Gait verification using probabilistic methods’. Proc. IEEE Workshop on Applications of Computer Vision, Colorado, USA, January 2005, pp. 6065.
    18. 18)
      • 18. Lee, H., Hong, S., Kim, E.: ‘Neural network ensemble with probabilistic fusion and its application to gait recognition’, Neurocomputing, 2009, 72, (7–9), pp. 15571564.
    19. 19)
      • 19. Kale, A., Sundaresan, A., Rajagopalan, A.N., et al: ‘Identification of humans using gait’, IEEE Trans. Image Process., 2004, 13, (9), pp. 11631173.
    20. 20)
      • 20. Liu, Z., Sarkar, S.: ‘Improved gait recognition by gait dynamics normalization’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (6), pp. 863876.
    21. 21)
      • 21. Al-Huseiny, M.S., Mahmoodi, S., Nixon, M.S.: ‘Gait learning-based regenerative model: a level set approach’. Proc. Int. Conf. Pattern Recognition, Istanbul, Turkey, August 2010, pp. 26442647.
    22. 22)
      • 22. Creamers, D., Osher, S.J., Soatto, S.: ‘Kernel density estimation and intrinsic alignment for shape priors in level set segmentation’, Int. J. Comput. Vis., 2006, 69, (3), pp. 335351.
    23. 23)
      • 23. Creamers, D.: ‘Dynamical statistical shape priors for level set-based tracking’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (8), pp. 12621273.
    24. 24)
      • 24. Huang, Y., Xu, D., Nie, F.: ‘Regularized trace ratio discriminant analysis with patch distribution feature for human gait recognition’. Proc. IEEE Int. Conf. Image Processing, Brussels, Belguim, September 2010, pp. 24492452.
    25. 25)
      • 25. Xu, D., Huang, Y., Zeng, Z., Xu, X.: ‘Human gait recognition using patch distribution feature and locality-constrained group sparse representation’, IEEE Trans. Image Process., 2012, 21, (1), pp. 316326.
    26. 26)
      • 26. http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp, accessed July 2012.
    27. 27)
      • 27. Shutler, J., Grant, M., Nixon, M.S., Carter, J.: ‘On a large sequence-based human gait database’. Proc. Int. Conf. on Recent Advances in Soft Computing, Nottingham, UK, December 2002, pp. 6671.
    28. 28)
      • 28. http://www.gait.ecs.soton.ac.uk/database, accessed July 2012.
    29. 29)
      • 29. Sarkar, S., Phillips, P.J., Liu, Z., Vega, I.R., Grother, P., Bowyer, K.W.: ‘The humanID gait challenge problem: data sets, performance, and analysis’, IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27, (2), pp. 162177.
    30. 30)
      • 30. http://marathon.csee.usf.edu/GaitBaseline, accessed July 2012.
    31. 31)
      • 31. Bishop, C.M.: ‘Pattern recognition and machine learning’ (Springer, 2006).
    32. 32)
      • 32. Cheng, M.H., Ho, M.F., Huang, C.L.: ‘Gait analysis for human identification through manifold learning and HMM’, Pattern Recognit., 2008, 41, (8), pp. 25412553.
    33. 33)
      • 33. Boulgouris, N.V., Plataniotis, K.N., Hatzinakos, D.: ‘Gait recognition using dynamic time warping’. Proc. IEEE Int. Symp. on Multimedia Signal Processing, Siena, Italy, September 2004, pp. 263266.
    34. 34)
      • 34. Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: ‘The FERET evaluation methodology for face-recognition algorithms’, IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, (10), pp. 10901104.
    35. 35)
      • 35. Lee, H., Hong, S., Kim, E.: ‘An efficient gait recognition based on a selective neural network ensemble’, Int. J. Imaging Syst. Technol., 2008, 18, (4), pp. 237241.
    36. 36)
      • 36. Hong, S., Lee, H., Toh, K.-A., Kim, E.: ‘Gait recognition using multi-bipolarized contour vector’, Int. J. Control Autom. Syst., 2009, 7, (5), pp. 799808.
    37. 37)
      • 37. Boyd, J.E.: ‘Synchronization of oscillations for machine perception of gaits’, Comput. Vis. Image Underst., 2004, 96, (1), pp. 3559.
    38. 38)
      • 38. Lam, T.H.W., Lee, R.S.T., Zhang, D.: ‘Human gait recognition by the fusion of motion and static spatio-temporal templates’, Pattern Recognit., 2007, 40, (9), pp. 25632573.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2011.0234
Loading

Related content

content/journals/10.1049/iet-cvi.2011.0234
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address