Fast and reliable iris segmentation algorithm

Design a fast and reliable iris segmentation algorithm for less constrained iris images is essential to build a robust iris recognition system. Daugman's integrodifferential operator (IDO) is one of powerful iris segmentation mechanisms, but in contrast consumes a large portion of the computational time for localising the rough position of the iris centre and eyelid boundaries. To address this problem, a fast iris segmentation algorithm is proposed. First, the circular Gabor filter is adopted to find the rough position of the pupil centre. Second, the iris and pupil circles are localised using the IDO taken into account that the real centres of the iris and pupil are in the small area around the rough position of the pupil centre. Third, the upper and lower eyelid boundaries are extracted using the live-wire technique. Experimental results demonstrate that the proposed iris segmentation algorithm significantly minimises the required time to segment the iris without affecting the segmentation accuracy. Moreover, the comparison results with state-of-the-art iris segmentation algorithms show the superiority of the proposed algorithm in terms of segmentation accuracy and recognition performance. The challenging UBIRIS.v1 iris image database is utilised to evaluate the performance of the proposed algorithm.

[1]  William A. Barrett,et al.  Interactive live-wire boundary extraction , 1997, Medical Image Anal..

[2]  Libor Masek,et al.  MATLAB Source Code for a Biometric Identification System Based on Iris Patterns , 2003 .

[3]  Fabio Scotti,et al.  Noisy iris segmentation with boundary regularization and reflections removal , 2010, Image Vis. Comput..

[4]  Ghassan Hamarneh,et al.  Color lesion boundary detection using live wire , 2005, SPIE Medical Imaging.

[5]  Tianzi Jiang,et al.  A modified Gabor filter design method for fingerprint image enhancement , 2003, Pattern Recognit. Lett..

[6]  John Daugman.  How iris recognition works , 2004 .

[7]  Naphtali Rishe,et al.  A highly accurate and computationally efficient approach for unconstrained iris segmentation , 2010, Image Vis. Comput..

[8]  Peihua Li,et al.  Iris recognition in non-ideal imaging conditions , 2012, Pattern Recognit. Lett..

[9]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[10]  Tieniu Tan,et al.  Toward Accurate and Fast Iris Segmentation for Iris Biometrics , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[12]  Imran Usman,et al.  Iris localization in frontal eye images for less constrained iris recognition systems , 2012, Digit. Signal Process..

[13]  Ching Y. Suen,et al.  Towards nonideal iris recognition based on level set method, genetic algorithms and adaptive asymmetrical SVMs , 2011, Eng. Appl. Artif. Intell..

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

[15]  N.B. Puhan,et al.  Iris recognition on edge maps , 2007, 2007 6th International Conference on Information, Communications & Signal Processing.

[16]  Bedrich J. Hosticka,et al.  Unsupervised texture segmentation of images using tuned matched Gabor filters , 1995, IEEE Trans. Image Process..

[17]  Tieniu Tan,et al.  Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition , 2010, Image Vis. Comput..

[18]  Rui Chen,et al.  Iris segmentation for non-cooperative recognition systems , 2011 .

[19]  N. Sudha,et al.  Efficient segmentation technique for noisy frontal view iris images using Fourier spectral density , 2011, Signal Image Video Process..

[20]  Kang Ryoung Park,et al.  A new iris segmentation method for non-ideal iris images , 2010, Image Vis. Comput..

[21]  Dennis Gabor,et al.  Theory of communication , 1946 .

[22]  Anil K. Jain,et al.  Unsupervised texture segmentation using Gabor filters , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[23]  Richard P. Wildes,et al.  Iris recognition: an emerging biometric technology , 1997, Proc. IEEE.

[24]  H S Smallman,et al.  Fine grain of the neural representation of human spatial vision , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[25]  Arun Ross,et al.  Iris Segmentation Using Geodesic Active Contours , 2009, IEEE Transactions on Information Forensics and Security.

[26]  Luís A. Alexandre,et al.  UBIRIS: A Noisy Iris Image Database , 2005, ICIAP.

[27]  D. G. Albrecht,et al.  Spatial frequency selectivity of cells in macaque visual cortex , 1982, Vision Research.

[28]  Richa Singh,et al.  Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[29]  Ling Guan,et al.  Iris localization using local histogram and other image statistics , 2012 .

[30]  John Daugman.  High confidence recognition of persons by iris patterns , 2001, Proceedings IEEE 35th Annual 2001 International Carnahan Conference on Security Technology (Cat. No.01CH37186).

[31]  Wen Gao,et al.  A novel pupil localization method based on GaborEye model and radial symmetry operator , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[32]  Tieniu Tan,et al.  Invariant texture segmentation via circular Gabor filters , 2002, Object recognition supported by user interaction for service robots.

[33]  Yan-fang Zhang,et al.  A new eye location method based on Ring Gabor Filter , 2008, 2008 IEEE International Conference on Automation and Logistics.

[34]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

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

[36]  Luís A. Alexandre,et al.  Toward Noncooperative Iris Recognition: A Classification Approach Using Multiple Signatures , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[38]  W. Gareth J. Howells,et al.  A Survey of Point-Source Specular Reflections in Noisy Iris Images , 2010, 2010 International Conference on Emerging Security Technologies.

[39]  Libor Masek,et al.  Recognition of Human Iris Patterns for Biometric Identification , 2003 .

[40]  Jayaram K. Udupa,et al.  Adaptive boundary detection using 'live-wire' two-dimensional dynamic programming , 1992, Proceedings Computers in Cardiology.

[41]  Lee Luan Ling,et al.  Fast and Efficient Iris Image Segmentation , 2010 .

[42]  W. Gareth J. Howells,et al.  A Versatile Iris Segmentation Algorithm , 2011, BIOSIG.

[43]  William A. Barrett,et al.  Interactive Segmentation with Intelligent Scissors , 1998, Graph. Model. Image Process..

[44]  W. Gareth J. Howells,et al.  Are Two Eyes Better than One? An Experimental Investigation on Dual Iris Recognition , 2010, 2010 International Conference on Emerging Security Technologies.

[45]  Ching Y. Suen,et al.  Iris segmentation using game theory , 2012, Signal Image Video Process..

[46]  Natalia A. Schmid,et al.  On Techniques for Angle Compensation in Nonideal Iris Recognition , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

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

[48]  John Daugman,et al.  New Methods in Iris Recognition , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[49]  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.

[50]  Luís A. Alexandre,et al.  Iris segmentation methodology for non-cooperative recognition , 2006 .