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The Best Bits in an Iris Code
June 2009 (vol. 31 no. 6)
pp. 1-1
Iris biometric systems apply filters to iris images to extract information about iris texture. Daugman's approach maps the filter output to a binary iris code. The fractional Hamming distance between two iris codes is computed and decisions about the identity of a person are based on the computed distance. The fractional Hamming distance weights all bits in an iris code equally. However, not all the bits in an iris code are equally useful. Our research is the first to present experiments documenting that some bits are more consistent than others. Different regions of the iris are compared to evaluate their relative consistency, and contrary to some previous research, we find that the middle bands of the iris are more consistent than the inner bands. The inconsistent-bit phenomenon is evident across genders and different filter types. Possible causes of inconsistencies, such as segmentation, alignment issues, and different filters are investigated. The inconsistencies are largely due to the coarse quantization of the phase response. Masking iris code bits corresponding to complex filter responses near the axes of the complex plane improves the separation between the match and nonmatch Hamming distance distributions.

[1] K. Hollingsworth, K. Bowyer, and P. Flynn, “All Iris Code Bits Are Not Created Equal,” Proc. IEEE Int'l Conf. Biometrics: Theory, Applications, and Systems, Sept. 2007.
[2] J. Daugman, “How Iris Recognition Works,” IEEE Trans. Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 21-30, 2004.
[3] R.P. Wildes, “Iris Recognition,” Biometric Systems: Technology, Design and Performance Evaluation, pp. 63-95, Spring-Verlag, 2005.
[4] K.W. Bowyer, K.P. Hollingsworth, and P.J. Flynn, “Image Understanding for Iris Biometrics: A Survey,” Computer Vision and Image Understanding, vol. 110, no. 2, pp. 281-307, 2007.
[5] P.J. Phillips, W.T. Scruggs, A.J. O'Toole, P.J. Flynn, K.W. Bowyer, C.L. Schott, and M. Sharpe, “FRVT 2006 and ICE 2006 Large-Scale Results,” Technical Report NISTIR 7408, Nat'l Inst. Standards and Technology, http://iris.nist.govice, Mar. 2007.
[6] R.M. Bolle, S. Pankanti, J.H. Connell, and N. Ratha, “Iris Individuality: A Partial Iris Model,” Proc. 17th Int'l Conf. Pattern Recognition, pp. II: 927-930, 2004.
[7] Iris Challenge Evaluation 2005 Workshop Presentations, Nat'l Inst. Standards and Technology, http://iris.nist.gov/icepresenta tions.htm , 2008.
[8] LG, http:/www.lgiris.com/, Mar. 2009.
[9] X. Liu, K.W. Bowyer, and P.J. Flynn, “Experiments with an Improved Iris Segmentation Algorithm,” Proc. Fourth IEEE Workshop Automatic Identification Technologies, pp. 118-123, Oct. 2005.
[10] A.J. Glenstrup, “Eye Controlled Media: Present and Future State,” master's thesis, Univ. of Copenhagen, http://www.diku.dk/~panic/eyegazearticle.pdf , 1995.
[11] C.-L. Tisse, L. Martin, L. Torres, and M. Robert, “Person Identification Technique Using Human Iris Recognition,” Vision Interface, pp. 294-299, 2002.
[12] L. Ma, T. Tan, Y. Wang, and D. Zhang, “Local Intensity Variation Analysis for Iris Recognition,” Pattern Recognition, vol. 37, no. 6, pp. 1287-1298, Feb. 2004.
[13] K. Miyazawa, K. Ito, T. Aoki, K. Kobayashi, and H. Nakajima, “An Efficient Iris Recognition Algorithm Using Phase-Based Image Matching,” Proc. Int'l Conf. Image Processing, pp. II:49-52, 2005.
[14] Y. Du, B. Bonney, R. Ives, D. Etter, and R. Schultz, “Analysis of Partial Iris Recognition Using a 1-D Approach,” Proc. Int'l Conf. Acoustics, Speech, and Signal Processing, vol. 2, pp. ii:961-964, 2005.
[15] R. Broussard, L. Kennell, and R. Ives, “Identifying Discriminatory Information Content within the Iris,” Proc. SPIE, vol. 6944, pp.69440:T1-T11, 2008.
[16] J. Thornton, M. Savvides, and B.V.K. Vijaya Kumar, “An Evaluation of Iris Pattern Representations,” Proc. IEEE Int'l Conf. Biometrics: Theory, Applications, and Systems, Sept. 2007.
[17] E. Krichen, A. Mellakh, S. Salicetti, and B. Dorizzi, OSIRIS (Open Source for IRIS) Reference System, BioSecure Project, http:/www.biosecure.info, 2008.
[18] 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.

Index Terms:
Iris,Biometrics,Hamming distance,Band pass filters,Gabor filters,Ice,NIST,Information filtering,Information filters,Data mining,Feature Measurement,Computer vision
Citation:
"The Best Bits in an Iris Code," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 6, pp. 1-1, June 2009, doi:10.1109/TPAMI.2008.185
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