CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2008 vol.30 Issue No.10 - October

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Issue No.10 - October (2008 vol.30)

pp: 1741-1756

ABSTRACT

This paper presents an efficient algorithm for iris recognition using phase-based image matching --- an image matching technique using phase components in 2D Discrete Fourier Transforms (DFTs) of given images. Experimental evaluation using CASIA iris image databases (ver. 1.0 and ver. 2.0) and Iris Challenge Evaluation (ICE) 2005 database clearly demonstrates that the use of phase components of iris images makes possible to achieve highly accurate iris recognition with a simple matching algorithm. This paper also discusses major implementation issues of our algorithm. In order to reduce the size of iris data and to prevent the visibility of iris images, we introduce the idea of 2D Fourier Phase Code (FPC) for representing iris information. 2D FPC is particularly useful for implementing compact iris recognition devices using state-of-the-art DSP (Digital Signal Processing) technology.

INDEX TERMS

Biometrics, Image Processing and Computer Vision, Pattern Recognition, Signal processing, Iris Recognition, Phase-Based Image Matching, Phase-Only Correlation, Phase-Only Matched Filtering

CITATION

Kazuyuki Miyazawa, Koichi Ito, Takafumi Aoki, Koji Kobayashi, Hiroshi Nakajima, "An Effective Approach for Iris Recognition Using Phase-Based Image Matching",

*IEEE Transactions on Pattern Analysis & Machine Intelligence*, vol.30, no. 10, pp. 1741-1756, October 2008, doi:10.1109/TPAMI.2007.70833REFERENCES

- [1] J. Wayman, A. Jain, D. Maltoni, and D. Maio,
Biometric Systems. Springer, 2005.- [2] A. Jain, R. Bolle, and S. Pankanti,
Biometrics: Personal Identification in a Networked Society. Kluwer, 1999.- [6] 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.- [8] B.V.K. Vijaya Kumar, C. Xie, and J. Thornton, “Iris Verification Using Correlation Filters,”
Proc. Fourth Int'l Conf. Audio- and Video-Based Biometric Person Authentication, pp. 697-705, 2003.- [9] Z. Sun, T. Tan, and X. Qiu, “Graph Matching Iris Image Blocks with Local Binary Pattern,”
Advances in Biometrics, vol. 3832, pp.366-372, Jan. 2006.- [10] C.D. Kuglin and D.C. Hines, “The Phase Correlation Image Alignment Method,”
Proc. Int'l Conf. Cybernetics and Soc. '75, pp.163-165, 1975.- [11] 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.- [12] 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.- [13] K. Ito, H. Nakajima, K. Kobayashi, T. Aoki, and T. Higuchi, “A Fingerprint Matching Algorithm Using Phase-Only Correlation,”
IEICE Trans. Fundamentals, vol. 87-A, no. 3, pp. 682-691, Mar. 2004.- [14] K. Ito, A. Morita, T. Aoki, T. Higuchi, H. Nakajima, and K. Kobayashi, “A Fingerprint Recognition Algorithm Using Phase-Based Image Matching for Low-Quality Fingerprints,”
Proc. 12th IEEE Int'l Conf. Image Processing, vol. 2, pp. 33-36, Sept. 2005.- [15] K. Ito, A. Morita, T. Aoki, T. Higuchi, H. Nakajima, and K. Kobayashi, “A Fingerprint Recognition Algorithm Combining Phase-Based Image Matching and Feature-Based Matching,”
Advances in Biometrics, vol. 3832, pp. 316-325, Jan. 2006.- [16] H. Nakajima, K. Kobayashi, M. Morikawa, A. Katsumata, K. Ito, T. Aoki, and T. Higuchi, “Fast and Robust Fingerprint Identification Algorithm and Its Application to Residential Access Controller,”
Advances in Biometrics, vol. 3832, pp. 326-333, Jan. 2006.- [17] Products Using Phase-Based Image Matching, Graduate School of Information Sciences, Tohoku Univ., http://www.aoki.ecei. tohoku.ac.jp/research poc.html, 2008.
- [18] K. Miyazawa, K. Ito, T. Aoki, K. Kobayashi, and H. Nakajima, “An Efficient Iris Recognition Algorithm Using Phase-Based Image Matching,”
Proc. 12th IEEE Int'l Conf. Image Processing, vol. 2, pp.49-52, Sept. 2005.- [19] K. Miyazawa, K. Ito, T. Aoki, K. Kobayashi, and H. Nakajima, “A Phase-Based Iris Recognition Algorithm,”
Advances in Biometrics, vol. 3832, pp. 356-365, Jan. 2006.- [20] CASIA Iris Image Database, Inst. Automation, Chinese Academy of Sciences, http:/www.sinobiometrics.com/, 2008.
- [21] Iris Challenge Evaluation (ICE), Nat'l Inst. Standards and Technology, http://iris.nist.govice/, 2008.
- [22] R.C. Gonzalez and R.E. Woods,
Digital Image Processing, second ed. Prentice Hall, 2002.- [23] L. Masek and P. Kovesi, “Matlab Source Code for a Biometric Identification System Based on Iris Patterns,” School of Computer Science and Software Eng., Univ. of Western Australia, http://people.csse.uwa.edu.au/pk/studentprojects libor/, 2003.
- [24] ICE 2005 Results, Nat'l Inst. Standards and Technology, http://iris.nist.gov/ICEICE_2005_Results_30March2006.pdf , 2008.
- [28] B.V.K. Vijaya Kumar, M. Savvides, K. Venkataramani, and C. Xie, “Spatial Frequency Domain Image Processing for Biometric Recognition,”
Proc. Ninth IEEE Int'l Conf. Image Processing, vol. 1, pp. 53-56, 2002.- [30] B.V.K. Vijaya Kumar, A. Mahalanobis, and R. Juday,
Correlation Pattern Recognition. Cambridge Univ. Press, 2005. |