Advertisement

Multimedia Tools and Applications

, Volume 77, Issue 7, pp 8237–8257 | Cite as

Clothing-invariant human gait recognition using an adaptive outlier detection method

  • A. Ghebleh
  • M. Ebrahimi Moghaddam
Article
  • 161 Downloads

Abstract

Human gait as a behavioral biometric identifier has received much attention in recent years. But there are some challenges which hinder using this biometric in real applications. One of these challenges is clothing variations which complicates the recognition process. In this paper, we propose an adaptive outlier detection method to remove the effect of clothing on silhouettes. The proposed method detects the most similar parts of probe and each gallery sample independently and uses these parts to obtain a similarity measure. Towards this end, the distances of the probe and a gallery sample are calculated row by row which are then used to obtain an adaptive threshold to determine valid and invalid rows. The average distance per valid rows is then considered as dissimilarity measure of samples. Experimental results on OU-ISIR Gait Database, the Treadmill Dataset B and CASIA Gait Database, Dataset B, show that this method efficiently detects and removes the clothing effect on silhouettes and reaches about 82 and 84% successful recognition respectively.

Keywords

Biometrics Clothing-invariant Gait recognition Outlier detection 

References

  1. 1.
    Bashir K, Xiang T, Gong S (2008) Feature selection on gait energy image for human identification. in Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE Int Conf IEEEGoogle Scholar
  2. 2.
    Bashir K, Xiang T, Gong S (2010) Gait recognition without subject cooperation. Pattern Recogn Lett 31(13):2052–2060CrossRefGoogle Scholar
  3. 3.
    Bobick AF, Davis JW (2001) The recognition of human movement using temporal templates. Patt Anal Mach Int, IEEE Trans 23(3):257–267CrossRefGoogle Scholar
  4. 4.
    Bobick AF, Johnson AY (2001) Gait recognition using static, activity-specific parameters. in Computer Vision and Pattern Recognition. CVPR 2001. Proc IEEE Comput Soc Conf IEEEGoogle Scholar
  5. 5.
    Cunado D, Nixon MS, Carter JN (2003) Automatic extraction and description of human gait models for recognition purposes. Comput Vis Image Underst 90(1):1–41CrossRefGoogle Scholar
  6. 6.
    Dockstader SL, Berg MJ, Tekalp AM (2003) Stochastic kinematic modeling and feature extraction for gait analysis. Image Proc, IEEE Trans 12(8):962–976MathSciNetCrossRefGoogle Scholar
  7. 7.
    Guan Y, Li C-T, Hu Y (2012) Robust clothing-invariant gait recognition. Int Info Hiding Mult Sig Proc (IIH-MSP) Eighth Int Conf IEEEGoogle Scholar
  8. 8.
    Haiping LP, Konstantinos NV, Anastasios N (2008) A full-body layered deformable model for automatic model-based gait recognition. EURASIP J Adv Sign ProcGoogle Scholar
  9. 9.
    Han J, Bhanu B (2006) Individual recognition using gait energy image. Patt Anal Mach Int, IEEE Trans 28(2):316–322CrossRefGoogle Scholar
  10. 10.
    Hossain A, Makihara Y, Wang J, Yagi Y (2010) Clothing-invariant gait identification using part-based clothing categorization and adaptive weight control. Pattern Recogn 43(6):2281–2291CrossRefGoogle Scholar
  11. 11.
    Lam TH, Cheung KH, Liu JN (2011) Gait flow image: a silhouette-based gait representation for human identification. Pattern Recogn 44(4):973–987CrossRefzbMATHGoogle Scholar
  12. 12.
    Lee S, Liu Y, Collins R (2007) Shape variation-based frieze pattern for robust gait recognition. Comput Vis Patt Recog CVPR'07. IEEE Conf IEEEGoogle Scholar
  13. 13.
    Li X, CHEN Y (2013) Gait recognition based on structural gait energy image. J Comput Info Syst 9(1):121–126Google Scholar
  14. 14.
    Li X, Maybank SJ, Yan S, Tao D, Xu D (2008) Gait components and their application to gender recognition. Syst, Man, Cyber, Part C: Appl Rev, IEEE Trans 38(2):145–155CrossRefGoogle Scholar
  15. 15.
    Liu J, Zheng N (2007) Gait history image: a novel temporal template for gait recognition. in Multimedia and Expo. IEEE Int Conf IEEEGoogle Scholar
  16. 16.
    Makihara Y, Mannami H, Tsuji A, Hossain MA, Sugiura K, Mori A, Yagi Y (2012) The OU-ISIR gait database comprising the treadmill dataset. IPSJ Trans Comput Vis Appl 4:53–62CrossRefGoogle Scholar
  17. 17.
    Makihara Y, Sagawa R, Mukaigawa Y, Echigo T, Yagi Y (2006) Gait recognition using a view transformation model in the frequency domain, in Computer Vision–ECCV 2006. Springer 151–163Google Scholar
  18. 18.
    Piccardi M (2004) Background subtraction techniques: a review. Syst, Man Cyber IEEE Int conf IEEEGoogle Scholar
  19. 19.
    Rokanujjaman M, Hossain MA, Islam MR (2012) Effective part selection for part-based gait identification. Elect Comput Eng (ICECE) 2012 7th Int ConfGoogle Scholar
  20. 20.
    Rokanujjaman M, Hossain M, Islam M (2013) Effective part definition for gait identification using gait entropy image. Info, Elect Vis (ICIEV), Int Conf IEEEGoogle Scholar
  21. 21.
    Rokanujjaman M, Islam MS, Hossain MA, Islam MR, Makihara Y, Yagi Y (2013) Effective part-based gait identification using frequency-domain gait entropy features. Mult Tools Appl 1–22Google Scholar
  22. 22.
    Wang L, Ning H, Tan T, Hu W (2004) Fusion of static and dynamic body biometrics for gait recognition. Circ Syst Video Technol, IEEE Trans 14(2):149–158CrossRefGoogle Scholar
  23. 23.
    Wang L, Tan T, Ning H, Hu W (2003) Silhouette analysis-based gait recognition for human identification. Patt Anal Mach Int, IEEE Trans 25(12):1505–1518CrossRefGoogle Scholar
  24. 24.
    Whytock TP, Belyaev A, Robertson NM (2013) Towards Robust Gait Recognition. Adv Vis Comput. Springer 523–531Google Scholar
  25. 25.
    Yang X, Zhou Y, Zhang T, Shu G, Yang J (2008) Gait recognition based on dynamic region analysis. Signal Process 88(9):2350–2356CrossRefzbMATHGoogle Scholar
  26. 26.
    Yogarajah P, Condell JV, Prasad G (2011) P<inf>RW</inf>GEI: Poisson random walk based gait recognition. in Image and Signal Processing and Analysis (ISPA). 7th Int Symp IEEEGoogle Scholar
  27. 27.
    Yu S, Tan D, Tan T (2006) A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. Patt Recog. ICPR 2006. 18th Int Conf IEEEGoogle Scholar
  28. 28.
    Zhang R, Vogler C, Metaxas D (2007) Human gait recognition at sagittal plane. Image Vis Comput 25(3):321–330CrossRefGoogle Scholar
  29. 29.
    Zhang E, Zhao Y, Xiong W (2010) Active energy image plus 2DLPP for gait recognition. Signal Process 90(7):2295–2302CrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Faculty of Computer Science & EngineeringShahid Beheshti University: G, CTehranIran

Personalised recommendations