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BioMed Research International
Volume 2013 (2013), Article ID 481431, 14 pages
http://dx.doi.org/10.1155/2013/481431
Research Article

Optimized Periocular Template Selection for Human Recognition

Department of Computer Science and Engineering, National Institute of Technology Rourkela, Odisha 769008, India

Received 8 April 2013; Revised 30 June 2013; Accepted 7 July 2013

Academic Editor: Tatsuya Akutsu

Copyright © 2013 Sambit Bakshi 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. A. Sohail and P. Bhattacharya, “Detection of facial feature points using anthropometric face model,” Signal Processing for Image Enhancement and Multimedia Processing, vol. 31, pp. 189–200, 2008. View at Publisher · View at Google Scholar
  2. U. Park, R. R. Jillela, A. Ross, and A. K. Jain, “Periocular biometrics in the visible spectrum,” IEEE Transactions on Information Forensics and Security, vol. 6, no. 1, pp. 96–106, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. T. Camus and R. Wildes, “Reliable and fast eye finding in close-up images,” in Proceedings of the 16th International Conference on Pattern Recognition, vol. 1, pp. 389–394, 2002. View at Publisher · View at Google Scholar
  4. H. Sung, J. Lim, J.-H. Park, and Y. Lee, “Iris recognition using collarette boundary localization,” in Proceedings of the 17th International Conference on Pattern Recognition (ICPR '04), vol. 4, pp. 857–860, August 2004. View at Publisher · View at Google Scholar · View at Scopus
  5. B. Bonney, R. Ives, D. Etter, and Y. Du, “IRIS pattern extraction using bit planes and standard deviations,” in Proceedings of the 38th Asilomar Conference on Signals, Systems and Computers, vol. 1, pp. 582–586, November 2004. View at Publisher · View at Google Scholar · View at Scopus
  6. X. Liu, K. W. Bowyer, and P. J. Flynn, “Experiments with an improved iris segmentation algorithm,” in Proceedings of the 4th IEEE Workshop on Automatic Identification Advanced Technologies (AUTO ID '05), pp. 118–123, October 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. H. Proença and L. A. Alexandre, “Iris segmentation methodology for non-cooperative recognition,” IEE Proceedings: Vision, Image and Signal Processing, vol. 153, no. 2, pp. 199–205, 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. S. J. Pundlik, D. L. Woodard, and S. T. Birchfield, “Non-ideal iris segmentation using graph cuts,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR '08), pp. 1–6, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. Z. He, T. Tan, Z. Sun, and X. Qiu, “Toward accurate and fast iris segmentation for iris biometrics,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 9, pp. 1670–1684, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. J. Liu, X. Fu, and H. Wang, “Iris image segmentation based on K-means cluster,” in Proceedings of the IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS '10), vol. 3, pp. 194–198, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. F. Tan, Z. Li, and X. Zhu, “Iris localization algorithm based on gray distribution features,” in Proceedings of the 1st IEEE International Conference on Progress in Informatics and Computing (PIC '10), vol. 2, pp. 719–722, December 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. S. Bakshi, H. Mehrotra, and B. Majhi, “Real-time iris segmentation based on image morphology,” in Proceedings of the International Conference on Communication, Computing and Security (ICCCS '11), pp. 335–338, February 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. R. Abiantun and M. Savvides, “Tear-duct detector for identifying left versus right iris images,” in Proceedings of the 37th IEEE Applied Imagery Pattern Recognition Workshop (AIPR '08), pp. 1–4, October 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Bhat and M. Savvides, “Evaluating active shape models for eye-shape classification,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '08), pp. 5228–5231, April 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. J. Merkow, B. Jou, and M. Savvides, “An exploration of gender identification using only the periocular region,” in Proceedings of the 4th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS '10), September 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. J. R. Lyle, P. E. Miller, S. J. Pundlik, and D. L. Woodard, “Soft biometric classification using periocular region features,” in Proceedings of the 4th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS '10), September 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. K. Hollingsworth, K. W. Bowyer, and P. J. Flynn, “Identifying useful features for recognition in near-infrared periocular images,” in Proceedings of the 4th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS '10), September 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. D. L. Woodard, S. Pundlik, P. Miller, R. Jillela, and A. Ross, “On the fusion of periocular and iris biometrics in non-ideal imagery,” in Proceedings of the 20th International Conference on Pattern Recognition (ICPR '10), pp. 201–204, August 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. P. E. Miller, J. R. Lyle, S. J. Pundlik, and D. L. Woodard, “Performance evaluation of local appearance based periocular recognition,” in Proceedings of the 4th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS '10), September 2010. View at Publisher · View at Google Scholar · View at Scopus
  20. P. E. Miller, A. W. Rawls, S. J. Pundlik, and D. L. Woodard, “Personal identification using periocular skin texture,” in Proceedings of the 25th Annual ACM Symposium on Applied Computing (SAC '10), pp. 1496–1500, March 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. J. Adams, D. L. Woodard, G. Dozier, P. Miller, K. Bryant, and G. Glenn, “Genetic-based type II feature extraction for periocular biometric recognition: less is more,” in Proceedings of the 20th International Conference on Pattern Recognition (ICPR '10), pp. 205–208, August 2010. View at Publisher · View at Google Scholar · View at Scopus
  22. D. L. Woodard, S. J. Pundlik, P. E. Miller, and J. R. Lyle, “Appearance-based periocular features in the context of face and non-ideal iris recognition,” Signal, Image and Video Processing, vol. 5, no. 4, pp. 443–455, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. D. Malcik and M. Drahansky, “Anatomy of biometric passports,” Journal of Biomedicine and Biotechnology, vol. 2012, Article ID 490362, 8 pages, 2012. View at Publisher · View at Google Scholar
  24. V. Ramanathan and H. Wechsler, “Robust human authentication using appearance and holistic anthropometric features,” Pattern Recognition Letters, vol. 31, no. 15, pp. 2425–2435, 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. Y. Chen, M. Adjouadi, C. Han et al., “A highly accurate and computationally efficient approach for unconstrained iris segmentation,” Image and Vision Computing, vol. 28, no. 2, pp. 261–269, 2010. View at Publisher · View at Google Scholar · View at Scopus
  26. T. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of texture measures with classification based on feature distributions,” Pattern Recognition, vol. 29, no. 1, pp. 51–59, 1996. View at Publisher · View at Google Scholar · View at Scopus
  27. 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
  28. H. Proença, S. Filipe, R. Santos, J. Oliveira, and L. A. Alexandre, “The UBIRIS.v2: a database of visible wavelength iris images captured on-the-move and at-a-distance,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 8, pp. 1529–1535, 2010. View at Publisher · View at Google Scholar · View at Scopus
  29. P. Jonathon Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss, “The FERET evaluation methodology for face-recognition algorithms,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 10, pp. 1090–1104, 2000. View at Publisher · View at Google Scholar · View at Scopus
  30. H. Proença and L. A. Alexandre, “UBIRIS: a noisy iris image database,” in Proceedings of the 13th International Conference on Image Analysis and Processing, vol. 3617 of Lecture Notes in Computer Science, pp. 970–977, Springer, Cagliari, Italy, 2005. View at Scopus
  31. A. K. Jain, P. Flynn, and A. A. Ross, Handbook of Biometrics, Springer, New York, NY, USA, 2008.