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References

  1. S. Sun, L. Zhao, and S. Yang, “Gabor Weber local descriptor for bovine iris recognition,” Mathematical Problems in Engineering, vol. 2013, Article ID 920597, 7 pages, 2013.
Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 920597, 7 pages
http://dx.doi.org/10.1155/2013/920597
Research Article

Gabor Weber Local Descriptor for Bovine Iris Recognition

Institute of Systems Engineering, Southeast University, Sipailou 2, Nanjing 211189, China

Received 27 February 2013; Revised 12 April 2013; Accepted 29 April 2013

Academic Editor: Rongni Yang

Copyright © 2013 Shengnan Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. F. Madec, R. Geers, P. Vesseur, N. Kjeldsen, and T. Blaha, “Traceability in the pig production chain,” OIE Revue Scientifique et Technique, vol. 20, no. 2, pp. 523–537, 2001. View at Google Scholar · View at Scopus
  2. W. Shirou, “Cost-effective product traceability system based on widely distributed database,” Journal of Communications, vol. 2, no. 2, pp. 45–52, 2007. View at Google Scholar
  3. L. Zhao, S. Shengnan, and X. Wang, “Tracking and traceability system using livestock Iris code in meat supply chain,” International Journal of Innovative Computing, Information and Control, vol. 7, no. 5A, pp. 2201–2212, 2011. View at Google Scholar · View at Scopus
  4. J. G. Daugman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1148–1161, 1993. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Ross, “Iris recognition: the path forward,” Computer, vol. 43, no. 2, pp. 30–35, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. P. Li, X. Liu, and N. Zhao, “Weighted co-occurrence phase histogram for iris recognition,” Pattern Recognition Letters, vol. 33, no. 8, pp. 1000–1005, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. P. Yao, J. Li, X. Ye, Z. Zhuang, and B. Li, “Iris recognition algorithm using modified Log-Gabor filters,” in Proceedings of the 18th International Conference on Pattern Recognition (ICPR '06), pp. 461–464, August 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. R. P. Wildes, “Iris recognition: an emerging biometrie technology,” Proceedings of the IEEE, vol. 85, no. 9, pp. 1348–1363, 1997. View at Publisher · View at Google Scholar · View at Scopus
  9. L. Ma, T. Tan, Y. Wang, and D. Zhang, “Personal identification based on iris texture analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1519–1533, 2003. View at Publisher · View at Google Scholar · View at Scopus
  10. Z. Sun, T. Tan, and Y. Wang, “Robust encoding of local ordinal measures: a general framework of iris recognition,” in Proceedings of the International Workshop on Biometric Authentication, vol. 3087, pp. 270–282, 2004. View at Scopus
  11. D. M. Monro, S. Rakshit, and D. Zhang, “DCT-bsed iris recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 4, pp. 586–595, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. K. Miyazawa, K. Ito, T. Aoki, K. Kobayashi, and H. Nakajima, “An effective approach for Iris recognition using phase-based image matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 10, pp. 1741–1756, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. T. Ojala, M. Pietikäinen, and T. Mäenpää, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971–987, 2002. View at Publisher · View at Google Scholar · View at Scopus
  14. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004. View at Publisher · View at Google Scholar · View at Scopus
  15. E. Tola, V. Lepetit, and P. Fua, “DAISY: an efficient dense descriptor applied to wide-baseline stereo,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 5, pp. 815–830, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. Z. Sun, T. Tan, and X. Qiu, “Graph matching iris image blocks with local binary pattern,” in Proceedings of the International Conference on Biometrics, vol. 3832, pp. 366–372, 2006. View at Scopus
  17. C. Belcher and Y. Du, “Region-based SIFT approach to iris recognition,” Optics and Lasers in Engineering, vol. 47, no. 1, pp. 139–147, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Zhang, Z. Sun, and T. Tan, “Deformable DAISY matcher for robust iris recognition,” in Proceedings of the IEEE Conference on Image Processing, pp. 3250–3253, 2011.
  19. W. Zhang, S. Shan, W. Gao, X. Chen, and H. Zhang, “Local Gabor Binary Pattern Histogram Sequence (LGBPHS): a novel non-statistical model for face representation and recognition,” in Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV '05), pp. 786–791, October 2005. View at Publisher · View at Google Scholar · View at Scopus
  20. K. Takahashi, Y. Kuriya, and T. Morie, “Bicycle detection using pedaling movement by spatiotemporal Gabor filtering,” International Journal of Innovative Computing, Information and Control, vol. 8, no. 6, pp. 4059–4070, 2012. View at Google Scholar
  21. N. Dalal, B. Triggs, and C. Schmid, “Human detection using oriented histograms of flow and appearance,” in Proceedings of the European Conference on Computer Vision, vol. 3952, pp. 428–441, 2006. View at Publisher · View at Google Scholar · View at Scopus
  22. J. Chen, S. Shan, C. He et al., “WLD: a robust local image descriptor,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1705–1720, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. S. Sun and L. Zhao, “Bovine iris segmentation using region-based active contour model,” International Journal of Innovative Computing, Information and Control, vol. 8, no. 9, pp. 6461–6471, 2012. View at Google Scholar
  24. C. Liu and H. Wechsler, “Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition,” IEEE Transactions on Image Processing, vol. 11, no. 4, pp. 467–476, 2002. View at Publisher · View at Google Scholar · View at Scopus
  25. S. Sun and L. Zhao, “Efficient modified Weber local descriptor approach to bovine iris recognition,” ICIC Express Letters Part B, vol. 3, no. 5, pp. 1311–1317, 2012. View at Google Scholar
  26. Y. Rubner, J. Puzicha, C. Tomasi, and J. M. Buhmann, “Empirical evaluation of dissimilarity measures for color and texture,” Computer Vision and Image Understanding, vol. 84, no. 1, pp. 25–43, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  27. D. Gong, S. Li, and Y. Xiang, “Face recognition using the Weber local descriptor,” in Proceedings of the Asian Conference on Pattern Recognition, pp. 589–592, 2011.
  28. G. Lian, J. Lai, and Y. Yuan, “Fast pedestrian detection using a modified WLD detector in salient region,” in Proceedings of the International Conference on System Science and Engineering, pp. 564–569, 2001.
  29. Y. Song, S. Sun, and L. Zhao, “Learning center-epsilon local binary pattern for bovine iris recognition,” ICIC Express Letters, vol. 7, no. 4, pp. 1209–1214, 2013. View at Google Scholar
  30. S. Adwan and H. Abof, “Modified integral projection method for eye detection using dynamic time warping,” International Journal of Innovative Computing, Information and Control, vol. 8, no. 1A, pp. 187–200, 2012. View at Google Scholar
  31. A. Eftakhar, J. Tan, H. Kim, and S. Ishikawa, “An effective directional motion database organization for human motion recognition,” International Journal of Innovative Computing, Information and Control, vol. 8, no. 2, pp. 1359–1370, 2012. View at Google Scholar