A novel approach to image quality assessment in iris recognition systems

REFERENCES

• Yanushkevich, S. N. Synthetic biometrics: a survey, Proc. Int. Joint Conf. on Neural networks: IJCNN2006, Vancouver, BC, Canada, July 2006, IEEE, pp. 676–683.
   Google Scholar
• Miller, B. Vital signs of identity. IEEE Spectr., 1994, 31, 22–30.
   Web of Science ®Google Scholar
• International Biometric Group. ‘Independent testing of iris recognition technology’, Final report, International Biometric Group, New York, USA, 2005.
   Google Scholar
• Ma, L., Tan, T., Wang, Y. and Zhang, D. Personal identification based on iris texture analysis. IEEE Trans. Patt. Anal. Mach. Intell., 2003, 25, 1519–1533.
   Web of Science ®Google Scholar
• Daugman, J. How iris recognition works. IEEE Trans. Circuits Syst. Video Technol., 2004, 14, 21–30.
   Web of Science ®Google Scholar
• Chen, Y., Dass, S. C. and Jain, A. K. Localized iris image quality using 2-D wavelets, Proc. IEEE Int. Conf. on Biometrics: ICB 2006, Hong Kong, January 2006, IEEE, pp. 373–381.
   Google Scholar
• Kalka, N. D., Zuo, J. Y., Schmid, N. A. and Cukic, B. Image quality assessment for iris biometric. Proc. SPIE, 2006, 6202, 62020D.1–62020D.11.
   Google Scholar
• Bowyer, K. W., Hollingsworth, K. and Flynn, P. J. Image understanding for iris biometrics: a survey. Comput. Vision Image Understand., 2008, 110, 281–307.
   Web of Science ®Google Scholar
• Hollingsworth, K., Bowyer, K. W. and Flynn, P. J. Pupil dilation degrades iris biometric performance. Comput. Vision Image Understand., 2009, 113, 150–157.
   Web of Science ®Google Scholar
• Zuo, J. and Schmid, N. A. Robust iris segmentation algorithm for non-ideal iris imagery. IEEE Trans. Syst. Man Cynern. B, 2008.
   Google Scholar
• Schuckers, S. A. C., Schmid, N. A., Abhyankar, A., Dorairaj, V., Boyce, C. and Hornak, L. A. On techniques for angle compensation in nonideal iris recognition. IEEE Trans. Syst. Man Cybern. B, 2007, 37B, 1176–1190.
   Web of Science ®Google Scholar
• Vatsa, M., Singh, R. and Noore, A. Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing. IEEE Trans. Syst. Man Cybern. B, 2008, 38B, 1021–1035.
   Web of Science ®Google Scholar
• Kang, B. J. and Park, K. R. Real-time image restoration for iris recognition systems. IEEE Trans. Syst. Man Cybern. B, 2007, 37B, 1555–1566.
   Web of Science ®Google Scholar
• Thornton, J., Savvides, M. and Vijaya, B. A Bayesian approach to deformed pattern matching of iris images. IEEE Trans. Patt. Anal. Mach. Intell., 2007, 29, 596–606.
   PubMed Web of Science ®Google Scholar
• Zhang, G. and Salganicoff, M. ‘Method of measuring the focus of close-up image of eyes’, Technical report, US Patent 5953440, 1999.
   Google Scholar
• Zhu, X., Liu, Y., Ming, X. and Cui, Q. A quality evaluation method of iris images sequence based on wavelet coefficients in ‘region of interest’, Proc. 7th Int. Conf. on Computer and information technology: ICCIT 2004, Dhaka, Bangladesh, December 2004, Brac University, pp. 24–27.
   Google Scholar
• Belcher, C. and Du, Y. Information distance based selective feature clarity measure for iris recognition. Proc. SPIE, 2007, 6494, 64940E.1–64940E.12.
   Google Scholar
• Kang, B. J. and Park, J. R. A study on iris image restoration. Lect Notes Comput. Sci., 3546, 31–40.
   Google Scholar
• Lee, J. C., Huang, P. S., Chang, J. C., Chang, C. P. and Tu, T. M. Iris recognition using local texture analysis. Opt. Eng, 2008, 47, 067205–1–067205–10.
   Google Scholar
• Lee, J. C., Huang, P. S., Chang, C. P. and Tu, T. M. Novel and fast approach for iris location. J. Chung Cheng Inst. Technol., 2008, 36, 139–142.
   Google Scholar
• Chen, Y., Adjouadi, M., Han, C., Wang, J., Barreto, A., Rishe, N. and Andrian, J. A highly accurate and computationally efficient approach for unconstrained iris segmentation. Image Vision Comput., 2009, 28, 261–269.
   Web of Science ®Google Scholar
• Institute of Automation, Chinese Academy of Science. CASIA Iris Image Database, http://www.sinobiome-trics.com/chinese/chinese.htm
   Google Scholar
• National Institute of Science and Technology. Iris Challenge Evaluation, http://iris.nist.gov/ICE, 2005.
   Google Scholar
• Mahalanobis, P. and Mahalanobis, A. Statistical inference for automatic target recognition systems. Appl. Opt., 1994, 33, 6823–6825.
   PubMed Web of Science ®Google Scholar