Iris recognition: recent progress and remaining challenges

The increasing demand on enhanced security has led to an unprecedented interest in automated personal identification based on biometrics. Among the various biometric identification methods, iris recognition is widely regarded as the most reliable and is one of the most active research topics in biometrics. Significant progress has been made since the concept of automated iris recognition was first proposed in 1987, not only in research and algorithm development but also in commercial exploitation and practical applications. This paper provides an overview on recent progress in iris recognition and discusses some of the remaining challenges and possible future work in this exciting field.

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

[2]  Joseph P. Havlicek,et al.  AM-FM image segmentation , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

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

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

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

[6]  S.M. Elsherief,et al.  Biometric Personal Identification Based on Iris Recognition , 2006, 2006 International Conference on Computer Engineering and Systems.

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

[8]  R. Graczyk The eye. , 1955, Radiography.

[9]  Tieniu Tan,et al.  A fast and robust iris localization method based on texture segmentation , 2004, SPIE Defense + Commercial Sensing.

[10]  Tieniu Tan,et al.  Learning Based Resolution Enhancement of Iris Images , 2003, BMVC.

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

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

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

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

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

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

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

[18]  Robert L. Stevenson,et al.  Spatial Resolution Enhancement of Low-Resolution Image Sequences A Comprehensive Review with Directions for Future Research , 1998 .

[19]  Robert Tibshirani,et al.  Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy , 1986 .

[20]  Richard P. Wildes,et al.  Reliable and fast eye finding in close-up images , 2002, Object recognition supported by user interaction for service robots.

[21]  Tieniu Tan,et al.  Improving iris recognition accuracy via cascaded classifiers , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[22]  John Daugman,et al.  Demodulation by Complex-Valued Wavelets for Stochastic Pattern Recognition , 2003, Int. J. Wavelets Multiresolution Inf. Process..

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

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

[25]  Derrick Vail Physiology of the Eye: Clinical Application , 1960 .

[26]  Chengjun Liu,et al.  Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition , 2002, IEEE Trans. Image Process..

[27]  Arun Ross,et al.  Multibiometric systems , 2004, CACM.

[28]  Michael Elad,et al.  Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images , 1997, IEEE Trans. Image Process..

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

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

[31]  Tieniu Tan,et al.  Combining Face and Iris Biometrics for Identity Verification , 2003, AVBPA.

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

[33]  Sharath Pankanti,et al.  Evaluation techniques for biometrics-based authentication systems (FRR) , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[34]  James L. Wayman,et al.  Fundamentals of Biometric Authentication Technologies , 2001, Int. J. Image Graph..

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

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

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

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

[39]  Arun Ross,et al.  An introduction to biometric recognition , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[40]  Bruce A. Draper,et al.  A nonparametric statistical comparison of principal component and linear discriminant subspaces for face recognition , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[41]  Tieniu Tan,et al.  AN APPEARANCE-BASED METHOD FOR IRIS DETECTION , 2003 .

[42]  Takeo Kanade,et al.  Limits on super-resolution and how to break them , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).