Ordinal Measures for Iris Recognition

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.

[1]  John Daugman,et al.  Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns , 2001, International Journal of Computer Vision.

[2]  Boualem Boashash,et al.  A human identification technique using images of the iris and wavelet transform , 1998, IEEE Trans. Signal Process..

[3]  Shree K. Nayar,et al.  Ordinal Measures for Image Correspondence , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Javid Sadr,et al.  The Fidelity of Local Ordinal Encoding , 2001, NIPS.

[5]  Dexin Zhang,et al.  Personal Identification Based on Iris Texture Analysis , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  C. Sanchez-Avilaa,et al.  Two different approaches for iris recognition using Gabor filters and multiscale zero-crossing representation , 2004 .

[7]  B. V. K. Vijaya Kumar,et al.  A Bayesian Approach to Deformed Pattern Matching of Iris Images , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  P. Sinha,et al.  Dissociated Dipoles: Image Representation via Non-local Comparisons , 2003 .

[9]  John Daugman,et al.  High Confidence Visual Recognition of Persons by a Test of Statistical Independence , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Lionel Torres,et al.  Person Identification Technique Using Human Iris Recognition , 2002 .

[11]  Patrick J. Flynn,et al.  Comments on the CASIA version 1.0 Iris Data Set , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Dexin Zhang,et al.  Local intensity variation analysis for iris recognition , 2004, Pattern Recognit..

[13]  Pawan Sinha,et al.  Qualitative Representations for Recognition , 2002, Biologically Motivated Computer Vision.

[14]  Moncef Gabbouj,et al.  Texture similarity evaluation using ordinal co-occurrence , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[15]  S S Stevens,et al.  On the Theory of Scales of Measurement. , 1946, Science.

[16]  Pawan Sinha,et al.  Perceiving and recognizing three-dimensional forms , 1996 .

[17]  Tieniu Tan,et al.  An Iris Recognition Algorithm Using Local Extreme Points , 2004, ICBA.

[18]  Pawan Sinha,et al.  Receptive Field Structures for Recognition , 2006, Neural Computation.

[19]  Tieniu Tan,et al.  Robust direction estimation of gradient vector field for iris recognition , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[20]  Pawan Sinha,et al.  Qualitative Representations for Recognition , 2002, Biologically Motivated Computer Vision.

[21]  Mark J. T. Smith,et al.  Iris-Based Personal Authentication Using a Normalized Directional Energy Feature , 2003, AVBPA.

[22]  Jaihie Kim,et al.  A Novel Method to Extract Features for Iris Recognition System , 2003, AVBPA.

[23]  Tieniu Tan,et al.  Iris Recognition Based on Non-local Comparisons , 2004, SINOBIOMETRICS.

[24]  Dexin Zhang,et al.  Efficient iris recognition by characterizing key local variations , 2004, IEEE Transactions on Image Processing.

[25]  Ronald M. Lesperance,et al.  The Gaussian derivative model for spatial-temporal vision: I. Cortical model. , 2001, Spatial vision.

[26]  Okhwan Byeon,et al.  Efficient Iris Recognition through Improvement of Feature Vector and Classifier , 2001 .

[27]  W. Eric L. Grimson,et al.  Configuration based scene classification and image indexing , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[28]  I. Ohzawa,et al.  Spatiotemporal organization of simple-cell receptive fields in the cat's striate cortex. I. General characteristics and postnatal development. , 1993, Journal of neurophysiology.

[29]  Richard P. Wildes,et al.  A machine-vision system for iris recognition , 2005, Machine Vision and Applications.

[30]  Dexin Zhang,et al.  DCT-Based Iris Recognition , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Tieniu Tan,et al.  Robust Encoding of Local Ordinal Measures: A General Framework of Iris Recognition , 2004, ECCV Workshop BioAW.

[32]  M. Kendall.  Rank Correlation Methods , 1949 .

[33]  Sharath Pankanti,et al.  Error analysis of pattern recognition systems - the subsets bootstrap , 2004, Comput. Vis. Image Underst..

[34]  Rufin van Rullen,et al.  Rate Coding Versus Temporal Order Coding: What the Retinal Ganglion Cells Tell the Visual Cortex , 2001, Neural Computation.