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Issue No.12 - December (2009 vol.31)
pp: 2211-2226
Zhenan Sun , Center for Biometrics and Security Research, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing
Images of a human iris contain rich texture information useful for identity authentication. A key and still open issue in iris recognition is how best to represent such textural information using a compact set of features (iris features). In this paper, we propose using ordinal measures for iris feature representation with the objective of characterizing qualitative relationships between iris regions rather than precise measurements of iris image structures. Such a representation may lose some image-specific information, but it achieves a good trade-off between distinctiveness and robustness. We show that ordinal measures are intrinsic features of iris patterns and largely invariant to illumination changes. Moreover, compactness and low computational complexity of ordinal measures enable highly efficient iris recognition. Ordinal measures are a general concept useful for image analysis and many variants can be derived for ordinal feature extraction. In this paper, we develop multilobe differential filters to compute ordinal measures with flexible intralobe and interlobe parameters such as location, scale, orientation, and distance. Experimental results on three public iris image databases demonstrate the effectiveness of the proposed ordinal feature models.
Biometrics, feature representation, iris recognition, multilobe differential filter, ordinal measures.
Zhenan Sun, "Ordinal Measures for Iris Recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.31, no. 12, pp. 2211-2226, December 2009, doi:10.1109/TPAMI.2008.240
[1] 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.
[2] J. Daugman, “Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns,” Int'l J. Computer Vision, vol. 45, no. 1, pp. 25-38, 2001.
[3] R.P. Wildes, J.C. Asmuth, G.L. Green, S.C. Hsu, R.J. Kolczynski, J.R. Matey, and S.E. McBride, “A Machine-Vision System for Iris Recognition,” Machine Vision and Applications, vol. 9, pp. 1-8, 1996.
[4] W.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.
[5] L. Ma, T. Tan, Y. Wang, and D. Zhang, “Personal Identification Based on Iris Texture Analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1519-1533, Dec. 2003.
[6] 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.
[7] C. Tisse, L. Martin, L. Torres, and M. Robert, “Person Identification Technique Using Human Iris Recognition,” Proc. Int'l Conf. Vision Interface, pp. 294-299, 2002.
[8] J. Huang, L. Ma, Y. Wang, and T. Tan, “Iris Recognition Based on Local Orientation Description,” Proc. Sixth Asian Conf. Computer Vision, vol. 2, pp. 954-959, 2004.
[9] S. Noh, K. Bae, and J. Kim, “A Novel Method to Extract Features for Iris Recognition System,” Proc. Fourth Int'l Conf. Audio- and Video-Based Biometric Person Authentication, pp. 838-844, 2003.
[10] C. Sanchez-Avila and R. Sanchez-Reillo, “Two Different Approaches for Iris Recognition Using Gabor Filters and Multiscale Zero-Crossing Representation,” Pattern Recognition, vol. 38, no. 2, pp. 231-240, 2005.
[11] C. Park, J. Lee, M. Smith, and K. Park, “Iris-Based Personal Authentication Using a Normalized Directional Energy Feature,” Proc. Fourth Int'l Conf. Audio- and Video-Based Biometric Person Authentication, pp. 224-232, 2003.
[12] S. Lim, K. Lee, O. Byeon, and T. Kim, “Efficient Iris Recognition through Improvement of Feature Vector and Classifier,” ETRI J., vol. 23, no. 2, pp. 61-70, 2001.
[13] J. Thornton, M. Savvides, and V. Kumar, “A Bayesian Approach to Deformed Pattern Matching of Iris Images,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 4, pp. 596-606, Apr. 2007.
[14] L. Ma, T. Tan, D. Zhang, and Y. Wang, “Local Intensity Variation Analysis for Iris Recognition,” Pattern Recognition, vol. 37, no. 6, pp. 1287-1298, 2004.
[15] J. Cui, Y. Wang, T. Tan, L. Ma, and Z. Sun, “An Iris Recognition Algorithm Using Local Extreme Points,” Proc. First Int'l Conf. Biometric Authentication, pp. 442-449, 2004.
[16] D.M. Monro, S. Rakshit, and D. Zhang, “DCT-Based Iris Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 4, pp. 586-595, Apr. 2007.
[17] Z. Sun, Y. Wang, T. Tan, and J. Cui, “Robust Direction Estimation of Gradient Vector Field for Iris Recognition,” Proc. 17th Int'l Conf. Pattern Recognition, vol. 2, pp. 783-786, 2004.
[18] Int'l Biometrics Group, “Independent Testing of Iris Recognition Technology,” Final Report, ITIRT.html, May 2005.
[19] Iris Challenge Evaluation, http://iris.nist.govice/, 2009.
[20] P. Sinha, “Qualitative Representations for Recognition,” Lecture Notes in Computer Science, vol. 2525, pp. 249-262, 2002.
[21] S. Stevens, “On the Theory of Scales of Measurement,” Science, vol. 103, no. 2684, pp. 677-680, June 1946.
[22] M. Kendall and J.D. Gibbons, Rank Correlation Methods, fifth ed. Edward Ar nold, 1990.
[23] G.C. DeAngelis, I. Ohzawa, and R.D. Freeman, “Spatiotemporal Organization of Simple-Cell Receptive Fields in the Cat's Striate Cortex. I. General Characteristics and Postnatal Development,” J. Neurophysiology, vol. 69, no. 4, pp. 1091-1117, 1993.
[24] R. Van Rullen and S.J. Thorpe, “Rate Coding versus Temporal Order Coding: What the Retinal Ganglion Cells Tell the Visual Cortex,” Neural Computation, vol. 13, no. 6, pp. 1255-1283, 2001.
[25] P. Sinha, “Perceiving and Recognizing Three-Dimensional Forms,” PhD dissertation, Massachusetts Inst. of Tech nology, pp. 141-165, 1995.
[26] P. Lipson, E. Grimson, and P. Sinha, “Configuration Based Scene Classification and Image Indexing,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1007-1013, June 1997.
[27] D. Bhat and S. Nayar, “Ordinal Measures for Image Correspondence,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 4, pp. 415-423, Apr. 1998.
[28] M. Partio, B. Cramariuc, and M. Gabbouj, “Texture Similarity Evaluation Using Ordinal Co-Occurrence,” Proc. IEEE Int'l Conf. Image Processing, pp. 1537-1540, 2004.
[29] F. Smeraldi “Ranklets: A Complete Family of Multiscale, Orientation Selective Rank Features,” Research Report RR0309-01, Dept. of Computer Science, Queen Mary, Univ. of London, Sept. 2003.
[30] J. Sadr, S. Mukherjee, K. Thoresz, and P. Sinha, “The Fidelity of Local Ordinal Encoding,” Advances in Neural Information Processing Systems, T. Dietterich, S. Becker, and Z. Ghahramani, eds., MIT Press, 2002.
[31] K.J. Thoresz, “Qualitative Representations for Recognition,” master's thesis, Massachusetts Inst. of Tech nology, 2002.
[32] B. Balas and P. Sinha, “Dissociated Dipoles: Image Representation via Non-Local Comparisons,” CBCL Paper #229/AI Memo #2003-018, Massachusetts Inst. of Tech nology, 2003.
[33] B. Balas and P. Sinha, “Receptive Field Structures for Recognition,” CBCL Paper #246/AI Memo #2005-006, Massachusetts Inst. of Tech nology, 2005.
[34] R. Young, R. Lesperance, and W. Meyer, “The Gaussian Derivative Model for Spatial-Temporal Vision: I. Cortical Model,” Spatial Vision, vol. 14, nos. 3/4, pp. 261-319, 2001.
[35] University of Bath Iris Image Database, irisweb/, 2009.
[36] CASIA Iris Image Database, base.htm , 2009.
[37] N. Macmillan and C. Creelman, Detection Theory: A Users Guide. Cambridge Univ. Press, 1991.
[38] R.M. Bolle, N.K. Ratha, and S. Pankanti, “Error Analysis of Pattern Recognition Systems: The Subsets Bootstrap,” Computer Vision and Image Understanding, vol. 93, no. 1, pp. 1-33, 2004.
[39] Z. Sun, T. Tan, and Y. Wang, “Robust Encoding of Local Ordinal Measures: A General Framework of Iris Recognition,” Lecture Notes in Computer Science, vol. 3087, pp. 270-282, 2004.
[40] Z. Sun, T. Tan, and Y. Wang, “Iris Recognition Based on Non-Local Comparisons,” Lecture Notes in Computer Science, vol. 3338, pp. 67-77, 2004.
[41] P.J. Phillips, K.W. Bowyer, and P.J. Flynn, “Comments on the CASIA Version 1.0 Iris Data Set,” IEEE Trans. Pattern Analysis Machine Intelligence, vol. 29, no. 10, pp. 1869-1870, Oct. 2007.
[42] ISO/IEC 19794-6: 2005 Information technology—Biometric Data Interchange Formats—Part 6: Iris Image Data, 2005.
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