Abstract

Biometric means the identification of persons by their traits or characteristics. We give in this study a simple survey and a general review of gait recognition based on a skeleton model for human identification of recent gait progresses. Every individual has features; therefore, a biometric means a unique feature of any person. The methods of the recognition currently, such as face recognition, iris recognition or fingerprint recognition based, require a physical contact or cooperative subject. So it is difficult to identify individuals by using these methods at a distance. However, gait as a feature of persons walk does not have these constraints. Gait is, in fact, a novel biometric feature. It attempts to distinguish people by the way of their walk. It has been increasingly taken the attentions of the research workers. Recent years, gait has become a hot topic in computer vision with great development accomplished.

Keywords:

Biometric, gait, gait recognition approaches, skeleton model,

References

1. Ali, S., Z. Wu, M. Zhou, M.A. Asadi and H. Ahmed, 2013. Appraising the recital of joints in human running gait through 3d optical motion. TELKOMNIKA: Indones. J. Electr. Eng., 11(4): 1731-1740.
   CrossRef    
2. Brook, D. and D.A. Evans, 1972. An approach to the probability distribution of cusum run length. Biometrika, 59(3): 539-549.
   CrossRef    
3. Chai, Y., J. Ren, W. Han and H. Li, 2011. Human gait recognition: Approaches, datasets and challenges. Proceeding of the 4th International Conference on Imaging for Crime Detection and Prevention 2011 (ICDP, 2011), London, pp: 1-6.
   
4. Dikovski, B., G. Madjarov and D. Gjorgjevikj, 2014. Evaluation of different feature sets for gait recognition using skeletal data from kinect. Proceeding of the 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). Opatija, pp: 1304-1308.
   CrossRef    
5. Hofmann, M., S. Sural and G. Rigoll, 2011. Gait recognition in the presence of occlusion: A new dataset and baseline algorithms. Proceeding of the 19th International Conferences in Central Europe on Computer Graphics, Visualization and Computer Vision, pp: 99-104.
   
6. Hosseini, N.K. and M.J. Nordin, 2013. Human gait recognition: A silhouette based approach. J. Automat. Control Eng., 1(2): 259-267.
   
7. Joshi, A., M.S. Bhushan and M.J. Kaur, 2014. Gait recognition of human using SVM and BPNN classifiers. Int. J. Comput. Sci. Mobile Comput., 3(1): 281-290.
   
8. Li, Y.B. and Q. Yang, 2010. Gait extraction and recognition based on lower leg and ankle. Proceeding of the International Conference on Intelligent Computation Technology and Automation (ICICTA, 2010), Changsha, pp: 379-382.
   CrossRef    
9. Li, X. and Y. Chen, 2013. Gait recognition based on structural gait energy image. J. Comput. Inform. Syst., 9(1): 121-126.
   
10. Lu, W., W. Zong, W. Xing and E. Bao, 2014. Gait recognition based on joint distribution of motion angles. J. Visual Lang. Comput., 25(6): 754-763.
    CrossRef    
11. Ming, D., C. Zhang, Y. Bai, B. Wan, Y. Hu and K.D.K. Luk, 2009. Gait recognition based on multiple views fusion of wavelet descriptor and human skeleton model. Proceeding of the IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurements Systems (VECIMS'09). Hong Kong, pp: 246-249.
    CrossRef    
12. Mohan Kumar, H.P. and H.S. Nagendraswamy, 2014. Change energy image for gait recognition: an approach based on symbolic representation. Int. J. Imag. Graph. Signal Process., 6(4): 1-8.
    CrossRef    
13. Nixon, M.S., 2001. Southampton Human ID at a Distance Database. Retrieved from: http://www.gait.ecs.soton.ac.uk/data.php3. (Accessed on: August 17, 2015).
    Direct Link
14. Prathap, C. and S. Sakkara, 2015. Gait recognition using skeleton data. Proceeding of the International Conference on Advances in Computing, Communications and Informatics (ICACCI, 2015). Kochi, pp: 2302-2306.
    CrossRef    
15. Prerna, A. and Rajni, 2015. Survey on human gait recognition. Int. J. Eng. Res. Gen. Sci., 3(3): 492-497.
    
16. Sarkar, S. and Z. Liu, 2008. Gait Recognition. In: Jain, A.K., P. Flynn and A.A. Ross (Eds.), Handbook of Biometrics. Springer, US, pp: 109-129.
    CrossRef    PMid:18277093    
17. Singh, J.P. and S. Jain, 2010. Person identification based on gait using dynamic body parameters. Proceeding of the Trendz in Information Sciences and Computing (TISC, 2010). Chennai, pp 248-252.
    CrossRef    
18. Sivapalan, S., 2014. Human identification from video using advanced gait recognition techniques. Ph.D. Thesis, Queens land University of Technology.
    
19. Tanawongsuwan, R. and A. Bobick, 2001. Gait recognition from time-normalized joint-angle trajectories in the walking plane. Proceeding of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR, 2001), 2: II-726-II-731.
    CrossRef    
20. The Institute of Automation, 2001. CASIA Gait Database. Center for Biometrics and Security Research (CBSR). Retrieved from: http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp. (Accessed on: August 17, 2015).
    Direct Link
21. Wang, J., M. She, S. Nahavandi and A. Kouzani, 2010. A review of vision-based gait recognition methods for human identification. Proceeding of the International Conference on Digital Image Computing: Techniques and Applications (DICTA, 2010). Sydney, NSW, pp: 320-327.
    CrossRef    
22. Xiao, F., P. Hua, L. Jin and Z. Bin, 2010. Human gait recognition based on skeletons. Proceeding of the International Conference on Educational and Information Technology (ICEIT, 2010). Chongqing, 2: V2-83-V2-87.
    
23. Xiao, Q., 2007. Technology review - biometrics-technology, application, challenge, and computational intelligence solutions. IEEE Comput. Intell. M., 2(2): 5-25.
    CrossRef    
24. Xu, S.L. and Q.J. Zhang, 2010. Gait recognition using fuzzy principal component analysis. Proceeding of the 2nd International Conference on e-Business and Information System Security (EBISS, 2010). Wuhan, pp: 1-4.
    CrossRef    
25. Zhang, S., S. Bei, Y. Li and X. Shen, 2011. Gait identification method based on closed area feature of lower limbs. Proceeding of the International Conference on Electronic and Mechanical Engineering and Information Technology (EMEIT, 2011). Harbin, Heilongjiang, China, pp: 2056-2059.
    CrossRef    

Competing interests

The authors have no competing interests.

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