Gait is one of well-identified biometrics that has been broadly applied for human identification at a distance based on their motion style. However, the current gait recognition might have difficulties due to changing the viewing angles anduncertainty associated with gait signature extraction. This study deals with the design of an intelligent gait recognition system that tackles the problems mentioned above. This system is based on spatial-domain energy deviation image as a gait signature by adopting clustering technique to estimate the gait period in the gait sequence with arbitrary walking directions. To further improve the performance of the proposed system, interval type-2 fuzzy K-nearest neighbor classifier is used to diminish the effect of uncertainty formed by variations in gait signature extraction. Interval type-2 fuzzy set is involved in extending themembership values of each gait signature by using several initial K in order to handle and manage uncertainty that exists in choosing the initial value K. The proposed method realises the reduction in the dimensions of the gait feature and over-fitting. The comprehensive analyses reveal that the proposed algorithm can significantly enhance the multiple view gait recognition performance when being matched to the similar methods in the literature.

References

1. 1)
  - 22. Javier, C., Josep, A., Fernando, D.: ‘Robust normalization of silhouettes for recognition applications’, J. Pattern Recognit., 2004, 25, (5), pp. 591–601.
2. 2)
  - 16. Rhee, F., Hwang, C.: ‘An interval type-2 fuzzy K-nearest neighbor’. Int. Conf. on Fuzzy Systems, USA, 2003, pp. 802–807.
3. 3)
  - 9. Lu, J., Wang, G., Moulin, P.: ‘Human identity and gender recognition from gait sequences with arbitrary walking directions’, IEEE Trans. Inf. Forensics Sec., 2014, 9, (1), pp. 51–61.
4. 4)
  - 25. Rohit, K., Pathak, V.: ‘Gait recognition based on energy deviation image using fuzzy component analysis’, J. Innov. Manage. Technol., 2013, 4, (1), pp. 43–46.
5. 5)
  - 21. Venkata, G., Jilani, S.: ‘Fuzzy principal component analysis based gait recognition’, J. Comput. Sci. Inf. Technol., 2012, 3, (3), pp. 4015–4020.
6. 6)
  - 27. Yang, J., Zhang, D., Frangi, A., et al: ‘Two-dimensional PCA: a new approach to appearance-based face representation and recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2004, 26, (1), pp. 131–137.
7. 7)
  - 11. Yao, Z., Zhang, Z., Hu, M., et al: ‘Cross-view gait-based gender classification by transfer learning’. Advances in Multimedia Information Processing, 2013, pp. 79–87.
8. 8)
  - 4. Wang, J., She, M., Nahavandi, S., et al: ‘A review of vision-based gait recognition methods for human identification’. IEEE Int. Conf. on Digital Image Computing: Techniques and Applications, December 2010, pp. 320–327.
9. 9)
  - 6. Zheng, L., Zhang, Z., Wu, Q., et al: ‘Enhancing person re-identification by integrating gait biometric’, Neurocomputing, 2015, 168, pp. 1144–1156.
10. 10)
  - 24. Qinyong, M., Shenkang, W., Jianfeng, Q.: ‘Gait recognition at a distance based on energy deviation image’. 1st Int. Conf. on Bioinformatics and Biomedical Engineering, China, 2007, pp. 621–624.
11. 11)
  - 15. Kim, Y., Han, J.: ‘Fuzzy KNN algorithm using modified K-selection’. IEEE Int. Conf. on Fuzzy Systems, Japan, 1995, pp. 1673–1680.
12. 12)
  - 19. Seely, R., Goffredo, M., Carter, J., et al: ‘View invariant gait recognition’, in Jay Kuo, C.-C. (Eds.): ‘Handbook of remote biometrics’ (Springer, London, 2009), pp. 61–81.
13. 13)
  - 32. Cheng, Q., Fu, B., Chen, H.: ‘Gait recognition based on PCA and LDA’. Proc. of Second Symp. Int. in Computer Science and Computational Technology, 2009, pp. 124–127.
14. 14)
  - 8. Kusakunniran, W., Wu, Q., Li, H., et al: ‘Multiple views gait recognition using view transformation model based on optimized gait energy image’. 12th IEEE Int. Conf. on Computer Vision, 2009, pp. 1058–1064.
15. 15)
  - 7. Maodi, H., Wang, Y., Zhang, Z., et al: ‘Incremental learning for video-based gait recognition with LBP flow’, IEEE Trans. Cybern., 2013, 43, (1), pp. 77–89.
16. 16)
  - 2. Lee, L., Grimson, W.: ‘Gait analysis for recognition and classification’. Fifth IEEE Int. Conf. on Automatic Face and Gesture Recognition, May 2002, pp. 148–155.
17. 17)
  - 1. Gafurov, D.: ‘A survey of biometric gait recognition: approaches, security and challenges’. IEEE Int. Conf. on Biometrics: Theory, Applications and Systems, Norwegian, 2007, pp. 1–12.
18. 18)
  - 5. Maodi, H., Wang, Y., Zhang, Z., et al: ‘Gait-based gender classification using mixed conditional random field’, IEEE Trans. Syst. Man Cybern. B, Cybern., 2011, 41, (5), pp. 1429–1439.
19. 19)
  - 13. Lu, J., Zhang, E.: ‘Gait recognition for human identification based on ICA and fuzzy SVM through multiple views fusion’, Pattern Recognit. Lett., 2007, 28, (16), pp. 2401–2411.
20. 20)
  - 20. Kusakunniran, W., Wu, Q., Zhang, J., et al: ‘A new view-invariant feature for cross-view gait recognition’, IEEE Trans. Inf. Forensics Sec., 2013, 8, (10), pp. 1642–1653.
21. 21)
  - 29. Melin, P., Castillo, O.: ‘A review on the applications of type-2 fuzzy logic in classification and pattern recognition’, Expert Syst. Appl., 2013, 40, (13), pp. 5413–5423.
22. 22)
  - 10. Liu, N., Lu, J., Tan, Y.: ‘Joint subspace learning for view-invariant gait recognition’, IEEE Signal Process. Lett., 2011, 18, (7), pp. 431–434.
23. 23)
  - 31. Boulgouris, N., Zhiwei, X.: ‘Gait recognition using radon transform and linear discriminant analysis’, IEEE Trans. Image Process., 2007, 16, (3), pp. 731–740.
24. 24)
  - 28. Wang, K.-J., Liu, L., Ben, X., et al: ‘Gait recognition based on gait energy image and two dimensional principal component analysis’, J. Image Graph., 2009, 14, (12), pp. 2503–2509.
25. 25)
  - 30. Yang, W., Wang, J., Yang, J.: ‘Fuzzy 2-dimensional FLD for face recognition’, J. Inf. Comput. Sci., 2009, 4, (3), pp. 233–239.
26. 26)
  - 18. Kale, A., Chowdhury, A., Chellappa, R.: ‘Towards a view invariant gait recognition algorithm’. Proc. of IEEE Conf. in Advanced Video and Signal Based Surveillance, July 2003, pp. 143–150.
27. 27)
  - 26. Yang, J., Liu, C.: ‘Horizontal and vertical 2DPCA-based discriminant analysis for face verification on a large-scale database’, IEEE Trans. Inf. Forensics Sec., 2007, 2, (4), pp. 781–792.
28. 28)
  - 17. Qilian, L., Mendel, J.: ‘Interval type-2 fuzzy logic system: theory and design’, IEEE Trans. Fuzzy Syst., 2000, 8, (5), pp. 535–550.
29. 29)
  - 14. Liu, N., Tan, Y.: ‘View invariant gait recognition’. IEEE Int. Conf. on Acoustics Speech and Signal Processing, March 2010, pp. 1410–1413.
30. 30)
  - 23. Wang, L., Tan, T., Hu, W.: ‘Silhouette analysis-based gait recognition for human identification’, IEEE Trans. Pattern Anal. Mach. Intell., 2003, 25, (12), pp. 1505–1518.
31. 31)
  - 3. Hayder, A., Dargham, J., Ervin, G.M.: ‘Person identification using gait’, J. Comput. Electr. Eng., 2011, 3, (4), pp. 477–482.
32. 32)
  - 33. Sarkar, S., Phillips, P., Bowyer, K.: ‘The human gait challenge problem: data sets, performance, and analysis’, IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27, (2), pp. 162–177.
33. 33)
  - 12. Zhang, D., Wang, Y., Zhang, Z., et al: ‘Estimation of view angles for gait using a robust regression method’, J. Multimedia Tools Appl., 2013, 65, (3), pp. 419–439.
34. 34)
  - 34. Chieh, L., Chuang, C.-H., Wu, F., et al: ‘Cross view gait recognition by metric learning’. IEEE Int. Conf. on Consumer Electronics-Taiwan (ICCE-TW), 2014, pp. 81–82.

Design of adaptive biometric gait recognition algorithm with free walking directions

References

Related content