Efficient Iris Recognition Using Phase Code with Appropriate Preprocessing
Iris recognition is one of the most promising approaches in the area of biometric due to its high reliability for personal identification. The proposed approach provides an efficient iris recognition algorithm using phase-based image matching that is,an image matching technique using only the phase components in 2D Curvelet Transform of given images. This approach supports proper preprocessing steps to remove the irrelevant parts correctly from the given image and to extract only the iris region. The phase codes are generated by using curvelet transform from the extracted iris region. The phase based image matching technique provides a nified framework for high accuracy biometric authentication. Matching is done by block partitioning method. The phase only correlation(POC) matching algorithm is proposed for this approach. By using this matching algorithm by introducing a spatial ensemble averaging of the POC function is suitable for the degraded iris images.
J. Daugman, “High-confidence visual recognition of persons by a test of statistical independence,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1148-1161, Nov. 1993.
J. Daugman, Biometric personal identification system based on iris analysis, U.S. Patent 5 291 560, 1994.
J. Daugman, “Demodulation by complex-valued wavelets for stochastic pattern recognition”, Int. J.Wavelets Multi-Res. Info. Process. 1 (1), 1–17,2003.
R. Wildes, “Iris recognition: An emerging biometric technology”, Proc. IEEE, vol. 85, no. 9, pp. 1348-1363, Sept. 1997.
W. Boles and B. Boashash, “A human identification technique using images of the iris and wavelet transform,” IEEE Trans. Signal Processing,vol. 46, no. 4, pp. 1185-1188, Apr. 1998.
E. Cand’es, D. Donoho, Curvelets - a surprisingly effective nonadaptive representation for objects with edges, In Curves and Surface Fitting:Saint-Malo 1999, A. Cohen, C. Rabut, L. Schumaker (Eds.), Vanderbilt Univ. Press, Nashville, 105-120, 2000.
C. Tisse, L. Martin, L. Torres, and M. Robert, “Person identification technique using human iris recognition,” Proc. 15th Int’l Conf. Vision Interface, pp. 294-299, 2002.
E. Cand’es, L. Demanet, Curvelets and Fourier integral operators, C. R.Math. Acad. Sci. Paris, 336 (5), 395-398, 2003.
B. Kumar, C. Xie, J. Thornton, Iris verification using correlation filters,in: Proc. 4th Int’l Conf on Audio- and Video- Based Biometric Person Authentication, pp. 697–705, 2003.
K. Takita, T. Aoki, Y. Sasaki, T. Higuchi, and K. Kobayashi, “High-accuracy subpixel image registration based on phase-only correlation,” IEICE Trans. Fundamentals, vol. E86-A, no. 8, pp.1925-1934, Aug. 2003.
K. Takita, M.A. Muquit, T. Aoki, and T. Higuchi, “A sub-pixel correspondence search technique for computer vision applications,”IEICE Trans. Fundamentals, vol. 87-A, no. 8, pp. 1913-1923, Aug. 2004
L. Ma, T. Tan, Y. Wang, and D. .Zhang, “Efficient iris recognition by characterizing key local variations,” IEEE Trans. Image Processing, vol.13, no. 6, pp. 739-750, June 2004.
E. Cand’es, L. Demanet, D. Donoho, L. Ying, Fast discrete curvelet transforms, Multiscale Model. Simul. 5 (3), 861-899, March. 2006.
K. Miyazawa, K. Ito, and T. Aoki, K. Kobayashi and H. Nakajima, “ An effective approach for iris recognition using phase-based image matching,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol.30, no. 10, pp. 1741-1756, Oct. 2008
M. Nabti and A. Bouridane, “An effective and fast iris recognition based on a combined multiscale feature extraction technique”, Proceedings of Pattern Recognition, Vol: 41, pp: 808 – 879, 2008.
V. Velisavlievic, “Low complexity iris coding and recognition based on directionlets” IEEE Trans. on Information Forensics and Security, pp. 1-16, Apr. 2009.
L. Zhonghua and M. Hongyan, “ Iris recognition method based on the morlet wavelet transform real coefficients” International Symposium on Information Processing, pp. 113-116, Aug. 2009.
Xie, “Face Based on Curvelet Transform and LS-SVM” International Symposium on Information Processing, pp140-143, 2009.
CASIA Iris Image Database, Inst. Automation, Chinese Academy of Sciences, http://www.sinobiometrics.com/.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.