Gait Recognition Based on Modified Gait Energy Image

Israel Raul Tiñini Alvarez1, Guillermo Sahonero Alvarez1

Abstract

Biometric systems allow us to identify individuals from distinctive biological traits. Gait recognition is a biometric technique used to recognize humans based on the style of their walk. However, model-free based gait recognition performance is often degraded by the presence of some covariate factors such as view, clothing and carrying variations. From these, it has been shown that the change in appearance is the covariant that most affect the recognition performance. To address such issues, we propose to use a feature representation that takes both dynamic and static regions of silhouettes. This way, more robustness against covariates and better discriminative performance are expected. The proposed method is evaluated on one of the largest datasets available under the variations of clothing and carrying conditions: CASIA gait database B. Results show that the proposed method achieves correct classification rate up to 90% and outperformed state-of-the-art methods.

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References

  1. P. Karampelas and T. Bourlai, Surveillance in Action. 2018.
  2. H. Iwama, M. Okumura, Y. Makihara, and Y. Yagi, “The OU-ISIR gait database comprising the large population dataset and performance evaluation of gait recognition,” IEEE Trans. Inf. Forensics Secur., vol. 7, no. 5, pp. 1511–1521, 2012.
  3. J. Han and B. Bhanu, “Individual recognition using gait energy image,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 2, pp. 316–322, 2006.
  4. M. Tariq and M. A. Shah, “Review of Model-Free Gait Recognition in Biometric Systems,” 2017.
  5. G. V. Veres, L. Gordon, J. N. Carter, and M. S. Nixon, “What image information is important in silhouette-based gait recognition?,” Proc. 2004 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognition, 2004. CVPR 2004., vol. 2, pp. 776–782,
    2004.
  6. S. Das, U. Kumar, and S. Meher, “Improving Single View Gait Recognition Using Sparse Representation Based Classification,” pp. 317–321, 2016.
  7. D. Tao, X. Li, X. Wu, and S. J. Maybank, “General tensor discriminant analysis and Gabor features for gait recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 10, pp. 1700–1715, 2007.
  8. I. Rida, S. Almaadeed, and A. Bouridane, “Gait recognition based on modified phase-only correlation,” Signal, Image Video Process., vol. 10, no. 3, pp. 463–470, 2016.
  9. I. Rida, X. Jiang, and G. L. Marcialis, “Human body part selection by group lasso of motion for model-free gait recognition,” IEEE Signal Process. Lett., vol. 23, no. 1, pp. 154–158, 2016.
  10. I. Rida, S. Al-Maadeed, and A. Bouridane, “Unsupervised Feature Selection Method for Improved Human Gait Recognition,” pp. 1133–1137, 2015.
  11. I. Rida, L. Boubchir, N. Al-Maadeed, S. Al-Maadeed, and A. Bouridane, “Robust model-free gait recognition by statistical dependency feature selection and Globality-Locality Preserving Projections,” 2016 39th Int. Conf. Telecommun. Signal Process.
    TSP 2016
    , pp. 652–655, 2016.
  12. S. Yu, H. Chen, Q. Wang, L. Shen, and Y. Huang, “Invariant feature extraction for gait recognition using only one uniform model,” Neurocomputing, vol. 239, pp. 81–93, 2017.
  13. S. Sarkar, P. J. Phillips, Z. Liu, I. R. Vega, P. Grother, and K. W. Bowyer, “The humanID gait challenge problem: Data sets, performance, and analysis,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 2, pp. 162–177, 2005.
  14. S. Yu, Q. Wang, L. Shen, and Y. Huang, “View invariant gait recognition using only one uniform model,” Proc. – Int. Conf. Pattern Recognit., pp. 889–894, 2017.
  15. B. Khalid, T. Xiang, and S. Gong, “Gait recognition using gait entropy image,” 3rd Int. Conf. Imaging Crime Detect. Prev. (ICDP 2009), pp. P2–P2, 2009.
  16. Y. Dupuis, X. Savatier, and P. Vasseur, “Feature subset selection applied to model-free gait recognition,” Image Vis. Comput., vol. 31, no. 8, pp. 580–591, 2013.
  17. J.-H. Yoo, D. Hwang, K.-Y. Moon, and M. S. Nixon, “Automated Human Recognition by Gait using Neural Network,” 1st Work. Image Process. Theory, Tools Appl. (IPTA 2008), pp. 1–6, 2008.
  18. M. Alotaibi and A. Mahmood, “Automatic Real Time Gait Recognition based on Spatiotemporal Templates,” 2015.
  19. M. Alotaibi and A. Mahmood, “Improved Gait recognition based on specialized deep convolutional neural networks,” 2015 IEEE Appl. Imag. Pattern Recognit. Work., pp. 1–7, 2015.
  20. T. Yeoh, A. E. Hernan, and K. Tanaka, “Clothing-invariant Gait Recognition Using Convolutional Neural Network,” 2016 Int. Symp. Intell. Signal Process. Commun. Syst., pp. 1–5, 2016.
  21. D. S. Matovski, M. S. Nixon, S. Mahmoodi, and J. N. Carter, “The effect of time on gait recognition performance,” IEEE Trans. Inf. Forensics Secur., vol. 7, no. 2, pp. 543–552, 2012.
  22. X. Shi, Z. Guo, F. Nie, L. Yang, J. You, and D. Tao, “TwoDimensional Whitening Reconstruction for Enhancing Robustness of Principal Component Analysis,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 10, pp. 2130–2136, 2016.
  23. T. Chau, “A review of analytical techniques for gait data. Part 1: Fuzzy, statistical and fractal methods,” Gait Posture, vol. 13, no. 1, pp. 49–66, 2001.
  24. T. Chau, “A review of analytical techniques for gait data. Part 2: neural network and wavelet methods,” Gait Posture, vol. 13, no. 2, pp. 102–120, 2001.
  25. S. Yu, D. Tan, and T. Tan, “A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition,” Proc. – Int. Conf. Pattern Recognit., vol. 4, pp. 441– 444, 2006.
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