International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

A Survey on Segment Based Evaluation of Co- factor Used in Human Gait Recognition

Author : Dr. P.S.Smitha ¹ Sruthi Nath C ²

Date of Publication :12th October 2017

Abstract: Gait recognition is an effective biometric feature to identify persons from a distance by the way people walk or run. While gait has several attractive properties as a biometric, the greatest disadvantage in gait recognition is to identify an individual with cofactors and in different emotional and environmental conditions. Different views and angles of the camera also constitutes in degradation of the gait identification. Thus different measures and methods have been proposed for enhancing the gait identification in these cases. This paper is study of gait recognition during these conditions and discussing ways for lowering the degradations. This paper presents a literature review of cofactor affected gait recognition and propose a method to identify cofactor affected probes in certain view angles. Recent researches in gait identification techniques are presented in this paper.

Reference :

1. P. Tome, J. Fierrez, R. Vera-Rodriguez, and M. Nixon, “Soft biometrics and their application in person recognition at a distance,” IEEE Trans.Inf. Forensics Security, vol. 9, no. 3, pp. 464–475, Mar. 2014.
2. A. Kale, A. Roy-Chowdhury, and R. Chellappa, “Towards a view invariant gait recognition algorithm,” in Proc. IEEE Conf. Adv. Video Signal Based Surveil., Miami, FL, USA, 2003, pp. 143–150.
3. Y. Makihara, R. Sagawa, Y. Mukaigawa, T. Echigo, and Y. Yagi,“Gait recognition using a view transformation model in the frequency domain,” in Proc. 9th Eur. Conf. Comput. Vis., Graz, Austria, May 2006, pp. 151–163.
4. Effective part-based gait identification using frequency-domain gait entropy features - Multimedia Tools and Applications pp 3099–3120 (Springer)
5. Feature Selection On GAIT ENERGY IMAGE For Human Identification, Khalid Bashir, Tao Xiang, Shaogang Gong Queen Mary, University of London
6. M. A. Hossain, Y. Makihara, J. Wang, and Y. Yagi, “Clothes invariant gait identification using part-based adaptive weight control,” in Proc. 19th Int. Conf. Pattern Recognit., Tampa, FL, USA, Dec. 2008, pp. 1–4
7. W. Kusakunniran, Q. Wu, J. Zhang, and H. Li, “Gait recognition under various viewing angles based on correlated motion regression,” IEEE Trans. Circuits Syst. Video Technol., vol. 22, no. 6, pp. 966–980, Jun. 2012.
8. Y. Makihara, A. Tsuji, and Y. Yagi, “Silhouette transformation based on walking speed for gait identification,” in Proc. CVPR, San Francisco, CA, USA, Jun. 2010, pp. 717–722.
9. W. Kusakunniran, Q. Wu, J. Zhang, and H. Li, “Gait recognition across various walking speeds using higher order shape configuration based on a differential composition model,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 42, no. 6, pp. 1654–1668, Dec. 2012.
10. Y. Guan and C.-T. Li, “A robust speed-invariant gait recognition system for walker and runner identification,” in Proc. 6th IAPR Int. Conf. Biometr., Madrid, Spain, 2013, pp. 1–8.
11. M. A. Hossain, Y. Makihara, J. Wang, and Y. Yagi, “Clothing-invariant gait identification using part-based clothing categorization and adaptive weight control,” Pattern Recognit., vol. 43, no. 6, pp. 2281–2291, Jun. 2010.
12. S. Sarkar et al., “The humanid gait challenge problem: Data sets, performance, and analysis,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 2, pp. 162–177, Feb. 2005.
13. D. Tao, X. Li, S. J. Maybank, and X. Wu, “Human carrying status in visual surveillance,” in Proc. CVPR, vol. 2. New York, NY, USA, Jun. 2006, pp. 1670–1677.
14. B. Decann and A. Ross, “Gait curves for human recognition, backpack detection, and silhouette correction in a nighttime environment” Proc. SPIE vol. 7667, Biometr. Technol. Human Identif. VII, Apr. 2010, Art. ID 76670Q.
15. Y. Zhang et al., “Accelerometer-based gait recognition by sparse representation of signature points with clusters,” IEEE Trans. Cybern., to be published.
16. J. Han and B. Bhanu, “Individual recognition using gait energy image,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 2, pp. 316–322, Feb. 2006.
17. D. Tao, X. Li, X. Wu, and S. Maybank, “General tensor discriminant analysis and Gabor features for gait recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 10, pp. 1700–1715, Oct. 2007.
18. T. H. W. Lam, K. H. Cheung, and J. N. K. Liu, “Gait flow image: A silhouette-based gait representation for human identification,” Pattern Recognit., vol. 44, pp. 973–987, Apr. 2011.
19. M. Hu, Y. Wang, Z. Zhang, D. Zhang, and J. Little, “Incremental learning for video-based gait recognition with LBP flow,” IEEE Trans. Cybern., vol. 43, no. 1, pp. 77–89, Feb. 2013.
20. A. Bobick and A. Johnson, “Gait recognition using static activity specific parameters,” in Proc. CVPR, vol. 1. Kauai, HI, USA, 2001, pp. 423–430.
21. C. Yam, M. Nixon, and J. Carter, “Automated person recognition by walking and running via model-based approaches,” Pattern Recognit., vol. 37, no. 5, pp. 1057– 1072, 2004.
22. H.-D. Yang and S.-W. Lee, “Reconstruction of 3D human body pose for gait recognition,” in Proc. IAPR Int. Conf. Biometr., Hong Kong, Jan. 2006, pp. 619–625
23. G. Ariyanto and M. Nixon, “Marionette mass-spring model for 3D gait biometrics,” in Proc. 5th IAPR Int. Conf. Biometr., New Delhi, India, Mar. 2012, pp. 354– 359
24. M. Goffredo, I. Bouchrika, J. N. Carter, and M. S. Nixon, “Selfcalibrating view-invariant gait biometrics,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 40, no. 4, pp. 997–1008, Aug. 2010
25. G. Shakhnarovich, L. Lee, and T. Darrell, “Integrated face and gait recognition from multiple views,” in Proc. CVPR, vol. 1. Kauai, HI, USA, 2001, pp. 439–446.
26. L. Lee, “Gait analysis for classification,” Ph.D. dissertation, Dept. Comput. Sci. Eng., Massachusetts Inst. Technol., Cambridge, MA, USA 2002.
27. R. Bodor, A. Drenner, D. Fehr, O. Masoud, and N. Papanikolopoulos, “View-independent human motion classification using image-based reconstruction,” Image Vis. Comput., vol. 27, no. 8, pp. 1194–1206, 2009.
28. Y. Iwashita, R. Baba, K. Ogawara, and R. Kurazume, “Person identification from spatio-temporal 3D gait,” in Proc. Int. Conf. Emerg. Security Technol., Canterbury, U.K., 2010, pp. 30–35.
29. F. Jean, R. Bergevin, and A. B. Albu, “Computing and evaluating view normalized body part trajectories,” Image Vis. Comput., vol. 27, no. 9, pp. 1272–1284, Aug. 2009.
30. J. Han, B. Bhanu, and A. Roy-Chowdhury, “A study on view-insensitive gait recognition,” in Proc. IEEE Int. Conf. Image Process., vol. 3. Genoa, Italy, Sep. 2005, pp. 297–300.
31. J. Lu and Y.-P. Tan, “Uncorrelated discriminant simplex analysis for view-invariant gait signal computing,” Pattern Recognit. Lett., vol. 31, no. 5, pp. 382–393, 2010.
32. N. Liu, J. Lu, and Y.-P. Tan, “Joint subspace learning for view-invariant gait recognition,” IEEE Signal Process. Lett., vol. 18, no. 7, pp. 431–434, Jul. 2011.
33. W. Kusakunniran, Q. Wu, H. Li, and J. Zhang, “Multiple views gait recognition using view transformation model based on optimized gait energy image,” in Proc. IEEE 12th Int. Conf. Comput. Vis. Workshops, Kyoto, Japan, 2009, pp. 1058–1064.
34. W. Kusakunniran, Q. Wu, J. Zhang, and H. Li, “Multi-view gait recognition based on motion regression using multilayer perceptron,” in Proc. ICPR, Istanbul, Turkey, 2010, pp. 2186–2189.
35. W. Kusakunniran, Q. Wu, J. Zhang, and H. Li, “Support vector regression for multi-view gait recognition based on local motion feature selection,” in Proc. CVPR, San Francisco, CA, USA, 2010, pp. 974–981.