Table of Contents Author Guidelines Submit a Manuscript
Mobile Information Systems
Volume 2017, Article ID 1281020, 14 pages
https://doi.org/10.1155/2017/1281020
Research Article

An Accurate and Efficient User Authentication Mechanism on Smart Glasses Based on Iris Recognition

Department of Computer Science & Information Engineering, National Central University, Taoyuan 32001, Taiwan

Correspondence should be addressed to Yung-Hui Li; moc.liamg@iuhgnuy

Received 9 December 2016; Revised 21 March 2017; Accepted 11 April 2017; Published 13 July 2017

Academic Editor: Paolo Bellavista

Copyright © 2017 Yung-Hui Li and Po-Jen Huang. 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. The Verge, 2016, “How to fake a fingerprint and break into a phone,” https://www.youtube.com/watch?v=tj2Ty7WkGqk.
  2. J. Daugman, “How iris recognition works,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 21–30, 2004. View at Publisher · View at Google Scholar · View at Scopus
  3. 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
  4. J. Daugman, “The importance of being random: statistical principles of iris recognition,” Pattern Recognition, vol. 36, no. 2, pp. 279–291, 2003. View at Publisher · View at Google Scholar · View at Scopus
  5. 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
  6. J. Daugman, “New methods in iris recognition,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 37, no. 5, pp. 1167–1175, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. W. W. Boles and B. Boashash, “A human identification technique using images of the iris and wavelet transform,” IEEE Transactions on Signal Processing, vol. 46, no. 4, pp. 1185–1188, 1998. View at Publisher · View at Google Scholar · View at Scopus
  8. Y. Zhu, T. Tan, and Y. Wang, “Biometric personal identification based on iris patterns,” in Proceedings of 15th International Conference on Pattern Recognition (ICPR '00), vol. 2, pp. 801–804, September 2000. View at Publisher · View at Google Scholar
  9. C. Sanchez-Avila, R. Sanchez-Reillo, and D. De Martin-Roche, “Iris-based biometric recognition using dyadic wavelet transform,” IEEE Aerospace and Electronic Systems Magazine, vol. 17, no. 10, pp. 3–6, 2002. View at Publisher · View at Google Scholar · View at Scopus
  10. J. S. Arteaga-Falconi, H. Al Osman, and A. El Saddik, “ECG authentication for mobile devices,” IEEE Transactions on Instrumentation and Measurement, vol. 65, no. 3, pp. 591–600, 2016. View at Publisher · View at Google Scholar · View at Scopus
  11. S. J. Kang, S. Y. Lee, H. I. Cho, and H. Park, “ECG authentication system design based on signal analysis in mobile and wearable devices,” IEEE Signal Processing Letters, vol. 23, no. 6, pp. 805–808, 2016. View at Publisher · View at Google Scholar · View at Scopus
  12. T. Hoang, D. Choi, and T. Nguyen, “Gait authentication on mobile phone using biometric cryptosystem and fuzzy commitment scheme,” International Journal of Information Security, vol. 14, no. 6, pp. 549–560, 2015. View at Publisher · View at Google Scholar · View at Scopus
  13. V. M. Patel, R. Chellappa, D. Chandra, and B. Barbello, “Continuous user authentication on mobile devices: recent progress and remaining challenges,” IEEE Signal Processing Magazine, vol. 33, no. 4, pp. 49–61, 2016. View at Publisher · View at Google Scholar · View at Scopus
  14. E. Khoury, L. El Shafey, C. McCool, M. Günther, and S. Marcel, “Bi-modal biometric authentication on mobile phones in challenging conditions,” Image and Vision Computing, vol. 32, no. 12, pp. 1147–1160, 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. C. Galdi, M. Nappi, and J.-L. Dugelay, “Multimodal authentication on smartphones: combining iris and sensor recognition for a double check of user identity,” Pattern Recognition Letters, 2015. View at Publisher · View at Google Scholar · View at Scopus
  16. M.-K. Lee, “Security notions and advanced method for human shoulder-surfing resistant PIN-entry,” IEEE Transactions on Information Forensics and Security, vol. 9, no. 4, pp. 695–708, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. M.-K. Lee, J. B. Kim, and M. K. Franklin, “Enhancing the security of personal identification numbers with three-dimensional displays,” Mobile Information Systems, vol. 2016, Article ID 8019830, 2016. View at Publisher · View at Google Scholar · View at Scopus
  18. J.-N. Luo and M.-H. Yang, “A mobile authentication system resists to shoulder-surfing attacks,” Multimedia Tools and Applications, vol. 75, no. 22, pp. 14075–14087, 2015. View at Publisher · View at Google Scholar · View at Scopus
  19. X. Zhao, T. Feng, W. Shi, and I. A. Kakadiaris, “Mobile user authentication using statistical touch dynamics images,” IEEE Transactions on Information Forensics and Security, vol. 9, no. 11, pp. 1780–1789, 2014. View at Publisher · View at Google Scholar · View at Scopus
  20. M. Martinez-Diaz, J. Fierrez, and J. Galbally, “Graphical password-based user authentication with free-form doodles,” IEEE Transactions on Human-Machine Systems, vol. 46, no. 4, pp. 607–614, 2016. View at Publisher · View at Google Scholar · View at Scopus
  21. D. Sliney, D. A. Rosa, F. DeLori et al., “Adjustment of guidelines for exposure of the eye to optical radiation from ocular instruments: statement from a task group of the international commission on non-ionizing radiation protection (ICNIRP),” Applied Optics, vol. 44, no. 11, pp. 2162–2176, 2005. View at Publisher · View at Google Scholar · View at Scopus
  22. S. Barra, A. Casanova, F. Narducci, and S. Ricciardi, “Ubiquitous iris recognition by means of mobile devices,” Pattern Recognition Letters, vol. 57, pp. 66–73, 2015. View at Publisher · View at Google Scholar · View at Scopus
  23. R. Kerekes, B. Narayanaswamy, J. Thornton, M. Savvides, and B. V. K. Vijaya Kumar, “proceedings of the Graphical model approach to iris matching under deformation and occlusion,” in 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '07), Minneapolis, MN, USA, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  24. J. Thornton, M. Savvides, and B. V. K. V. Kumar, “A Bayesian approach to deformed pattern matching of iris images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 4, pp. 596–606, 2007. View at Publisher · View at Google Scholar · View at Scopus
  25. W. K. Kong and D. Zhang, “Accurate iris segmentation based on novel reflection and eyelash detection model,” in proceedings of the 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing (ISIMP '01), pp. 263–266, May 2001. View at Scopus
  26. Y.-H. Li and M. Savvides, “Automatic iris mask refinement for high performance iris recognition,” in proceedings of the 2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications (CIB '09), pp. 52–58, April 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. Y.-H. Li and M. Savvides, “A pixel-wise, learning-based approach for occlusion estimation of iris images in polar domain,” in proceedings of the 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '09), pp. 1357–1360, April 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. Y.-H. Li and M. Savvides, “Fast and robust probabilistic inference of iris mask,” in SPIE Defense & Security Symposium on Biometric Identification Technologies, Proc. SPIE 7306, 730621, 2009.
  29. Y.-H. Li and M. Savvides, “An automatic iris occlusion estimation method based on high-dimensional density estimation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 4, pp. 784–796, 2013. View at Publisher · View at Google Scholar · View at Scopus
  30. S. Lim, K. Lee, O. Byeon, and T. Kim, “Efficient iris recognition through improvement of feature vector and classifier,” ETRI Journal, vol. 23, no. 2, pp. 61–70, 2001. View at Publisher · View at Google Scholar · View at Scopus
  31. M. De Marsico, M. Nappi, D. Riccio, and H. Wechsler, “Mobile Iris Challenge Evaluation (MICHE)-I, biometric iris dataset and protocols,” Pattern Recognition Letters, vol. 57, pp. 17–23, 2015. View at Publisher · View at Google Scholar · View at Scopus