Robust Encoding of Local Ordinal Measures: A General Framework of Iris Recognition

The randomness of iris pattern makes it one of the most reliable biometric traits. On the other hand, the complex iris image structure and various sources of intra-class variations result in the difficulty of iris representation. Although diverse iris recognition methods have been proposed, the fundamentals of iris recognition have not a unified answer. As a breakthrough of this problem, we found that several accurate iris recognition algorithms share a same idea — local ordinal encoding, which is the representation well-suited for iris recognition. After further analysis and summarization, a general framework of iris recognition is formulated in this paper. This work discovered the secret of iris recognition. With the guidance of this framework, a novel iris recognition method based on robust estimating the direction of image gradient vector is developed. Extensive experimental results demonstrate our idea.

[1]  John G. Daugman,et al.  Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression , 1988, IEEE Trans. Acoust. Speech Signal Process..

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

[3]  B. V. K. Vijaya Kumar,et al.  Iris Verification Using Correlation Filters , 2003, AVBPA.

[4]  J. Daugman.  Two-dimensional spectral analysis of cortical receptive field profiles , 1980, Vision Research.

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

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

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

[8]  Pietro Perona,et al.  Orientation diffusions , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[10]  Thomas S. Huang,et al.  Image processing , 1971 .

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

[12]  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.

[13]  Jaihie Kim,et al.  Iris Feature Extraction Using Independent Component Analysis , 2003, AVBPA.

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

[15]  Seong-Whan Lee,et al.  Biologically Motivated Computer Vision , 2002, Lecture Notes in Computer Science.

[16]  David Chandler,et al.  Biometric Product Testing Final Report , 2001 .

[17]  John Daugman.  Recognising Persons by Their Iris Patterns , 2004, SINOBIOMETRICS.

[18]  D. Ruppert.  Robust Statistics: The Approach Based on Influence Functions , 1987 .

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

[20]  Tomaso A. Poggio,et al.  A general framework for object detection , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

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

[22]  Azriel Rosenfeld,et al.  Robust regression methods for computer vision: A review , 1991, International Journal of Computer Vision.

[23]  Carmen Sanchez-Avila,et al.  Iris-based biometric recognition using dyadic wavelet transform , 2002 .

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

[25]  David Zhang,et al.  Automated Biometrics: Technologies and Systems , 2000 .

[26]  Tieniu Tan,et al.  Biometric personal identification based on iris patterns , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

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

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

[29]  Josef Kittler,et al.  Audio- and Video-Based Biometric Person Authentication, 5th International Conference, AVBPA 2005, Hilton Rye Town, NY, USA, July 20-22, 2005, Proceedings , 2005, AVBPA.

[30]  T. Tan,et al.  Iris Recognition Based on Multichannel Gabor Filtering , 2002 .

[31]  Guillermo Sapiro,et al.  Diffusion of General Data on Non-Flat Manifolds via Harmonic Maps Theory: The Direction Diffusion Case , 2000, International Journal of Computer Vision.

[32]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

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

[34]  Panos E. Trahanias,et al.  Vector directional filters-a new class of multichannel image processing filters , 1993, IEEE Trans. Image Process..

[35]  Charles V. Stewart,et al.  Robust Parameter Estimation in Computer Vision , 1999, SIAM Rev..

[36]  Tieniu Tan,et al.  Iris recognition using circular symmetric filters , 2002, Object recognition supported by user interaction for service robots.

[37]  Carmen Sánchez Ávila,et al.  Iris Recognition with Low Template Size , 2001, AVBPA.

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

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