Gait-based Recognition for Human Identification using Fuzzy Local Binary Patterns

Amer G. Binsaadoon, El-Sayed M. El-Alfy

2016

Abstract

With the increasing security breaches nowadays, automated gait recognition has recently received increasing importance in video surveillance technology. In this paper, we propose a method for human identification at distance based on Fuzzy Local Binary Pattern (FLBP). After the Gait Energy Image (GEI) is generated as a spatiotemporal summary of a gait video sequence, a multi-region partitioning is applied and FLBP based features are extracted for each region. We also evaluate the performance under the variation of some factors including viewing angle, clothing and carrying conditions. The experimental work showed that GEI-FLBP with partitioning has remarkably enhanced the identification accuracy.

References

  1. Abdelkader, C. B. (2002). Stride and cadence as a biometric in automatic person identification and verification. In Proc. 5th IEEE International Conf. on Automatic Face and Gesture Recognition.
  2. Ahonen, T., Hadid, A., and Pietikainen, M. (2006). Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12):2037-2041.
  3. Bhanu, B. and Han, J. (2002). Individual recognition by kinematic-based gait analysis. In Proceedings of 16th International Conference on Pattern Recognition.
  4. Brahnam, S., Jain, L. C., Nanni, L., and Lumini, A. (2014). Local Binary Patterns - New Variants and Applications. Springer-Verlag Berlin Heidelberg 2014.
  5. Chen, C., Zhang, J., and Fleischer, R. (2010). Distance approximating dimension reduction of riemannian manifolds. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 40(1):208-217.
  6. Han, J. and Bhanu, B. (2006a). Individual recognition using gait energy image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(2):316-322.
  7. Han, J. and Bhanu, B. (2006b). Individual recognition using gait energy image. IEEE Trans. Pattern Analysis and Machine Intelligence, 28(2):316-322.
  8. Ho, M.-F., Chen, K.-Z., and Huang, C.-L. (2009). Gait analysis for human walking paths and identities recognition. In IEEE International Conference on Multimedia and Expo (ICME).
  9. Hsu, C.-W. and Lin, C.-J. (2002). A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Networks, 13(2):415-425.
  10. Hu, M., Wang, Y., Zhang, Z., Zhang, D., and Little, J. (2013). Incremental learning for video-based gait recognition with lbp flow. IEEE Transactions on Cybernetics, 43(1):77-89.
  11. Huang, D.-Y., Lin, T.-W., Hu, W.-C., and Cheng, C.-H. (2013). Gait recognition based on gabor wavelets and modified gait energy image for human identification. Journal of Electronic Imaging, 22(4).
  12. Iakovidis, D., Keramidas, E., and Maroulis, D. (2008). Fuzzy local binary patterns for ultrasound texture characterization. In Campilho, A. and Kamel, M., editors, Image Analysis and Recognition, volume 5112 of Lecture Notes in Computer Science, pages 750-759. Springer Berlin Heidelberg.
  13. Kale, A., Chowdhury, A., and Chellappa, R. (2003). Towards a view invariant gait recognition algorithm. In Proceedings of IEEE Conference on Advanced Video and Signal Based Surveillance.
  14. Kale, A., Rajagopalan, A., Cuntoor, N., and Kruger, V. (2002). Gait-based recognition of humans using continuous hmms. In Proc. 5th IEEE International Conf. on Automatic Face and Gesture Recognition.
  15. Kale, A., Sundaresan, A., Rajagopalan, A., Cuntoor, N., Roy-Chowdhury, A., Kruger, V., and Chellappa, R. (2004). Identification of humans using gait. IEEE Transactions on Image Processing, 13(9):1163-1173.
  16. Kellokumpu, V., Zhao, G., Li, S., and Pietikäinen, M. (2009). Dynamic texture based gait recognition. In Advances in Biometrics, volume 5558 of Lecture Notes in Computer Science. Springer Berlin Heidelberg.
  17. Lee, L. (2001). Gait dynamics for recognition and classification. In Proceedings of the 5th IEEE International Conference on Automatic Face and Gesture Recognition (AFGR).
  18. Lee, L. and Grimson, W. (2002). Gait analysis for recognition and classification. In Proceedings of Fifth IEEE International Conference on Automatic Face and Gesture Recognition, pages 148-155.
  19. Lee, T. K. M., Belkhatir, M., and Sanei, S. (2014). A comprehensive review of past and present vision-based techniques for gait recognition. Multimedia Tools and Applications, 72(3):2833-2869.
  20. Li, C.-R., Li, J.-P., Yang, X.-C., and Liang, Z.-W. (2012). Gait recognition using the magnitude and phase of quaternion wavelet transform. In International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP).
  21. Lu, J. and Zhang, E. (2007). Gait recognition for human identification based on {ICA} and fuzzy {SVM} through multiple views fusion. Pattern Recognition Letters, 28(16):2401 - 2411.
  22. Mansur, A., Makihara, Y., Muramatsu, D., and Yagi, Y. (2014). Cross-view gait recognition using viewdependent discriminative analysis. In IEEE International Joint Conference on Biometrics (IJCB).
  23. Niyogi, S. and Adelson, E. (1994). Analyzing and recognizing walking figures in xyt. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 469-474.
  24. Nizami, I. F., Hong, S., Lee, H., Lee, B., and Kim, E. (2010). Automatic gait recognition based on probabilistic approach. International Journal of Imaging Systems and Technology, 20(4):400-408.
  25. Ojala, T., Pietikainen, M., and Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7):971-987.
  26. Ran, Y., Weiss, I., Zheng, Q., and Davis, L. (2007). Pedestrian detection via periodic motion analysis. International Journal of Computer Vision, 71(2).
  27. Sarkar, S., Phillips, P., Liu, Z., Vega, I., Grother, P., and Bowyer, K. (2005). The humanid gait challenge problem: data sets, performance, and analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(2):162-177.
  28. Wang, K., Xing, X., Yan, T., and Lv, Z. (2014). Couple metric learning based on separable criteria with its application in cross-view gait recognition. In Biometric Recognition, volume 8833 of Springer Lecture Notes in Computer Science, pages 347-356.
  29. Wang, L., Hu, W., and Tan, T. (2002). A new attempt to gait-based human identification. In Proc. 16th International Conference on Pattern Recognition.
  30. Yu, S., Tan, D., and Tan, T. (2006). A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In Proc. 18th International Conf. on Pattern Recognition, volume 4, pages 441-444.
  31. Zhang, E., Zhao, Y., and Xiong, W. (2010). Active energy image plus 2dlpp for gait recognition. Signal Processing, 90(7):2295 - 2302.
  32. Zhao, G. and Pietikainen, M. (2007). Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6):915-928.
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Paper Citation


in Harvard Style

Binsaadoon A. and El-Alfy E. (2016). Gait-based Recognition for Human Identification using Fuzzy Local Binary Patterns . In Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-172-4, pages 314-321. DOI: 10.5220/0005693103140321


in Bibtex Style

@conference{icaart16,
author={Amer G. Binsaadoon and El-Sayed M. El-Alfy},
title={Gait-based Recognition for Human Identification using Fuzzy Local Binary Patterns},
booktitle={Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2016},
pages={314-321},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005693103140321},
isbn={978-989-758-172-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Gait-based Recognition for Human Identification using Fuzzy Local Binary Patterns
SN - 978-989-758-172-4
AU - Binsaadoon A.
AU - El-Alfy E.
PY - 2016
SP - 314
EP - 321
DO - 10.5220/0005693103140321