A New User Dependent Iris Recognition System Based on an Area Preserving Pointwise Level Set Segmentation Approach

This paper presents a new user dependent approach in iris recognition systems. In the proposed method, consistent bits of iris code are calculated, based on the user specifications, using the user's mask. Another contribution of our work is in the iris segmentation phase, where a new pointwise level set approach with area preserving has been used for determining inner and outer iris boundaries, both exclusively performed in one step. Thanks to the special properties of this segmentation technique, there is no constraint about angles of head tilt. Furthermore, we showed that this algorithm is robust in noisy situations and can locate irises which are partly occluded by eyelid and eyelashes. Experimental results, on three renowned iris databases (CASIAIrisV3, Bath, and Ubiris), show that our method outperforms some of the existing methods, both in terms of accuracy and response time.

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

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

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

[4]  Bin Li,et al.  Iris Recognition Algorithm Using Modified Log-Gabor Filters , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[5]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

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

[7]  Tieniu Tan,et al.  Learning Based Enhancement Model of Iris , 2003 .

[8]  A. Ross,et al.  Segmenting Non-Ideal Irises Using Geodesic Active Contours , 2006, 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference.

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

[10]  Olivier D. Faugeras,et al.  Maintaining the point correspondence in the level set framework , 2006, J. Comput. Phys..

[11]  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.

[12]  Olivier D. Faugeras,et al.  Area Preserving Cortex Unfolding , 2004, MICCAI.

[13]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods , 1999 .

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

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

[16]  Mohammad Shahram Moin,et al.  A new approach for iris localization in iris recognition systems , 2008, 2008 IEEE/ACS International Conference on Computer Systems and Applications.

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

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

[19]  David Zhang,et al.  Detecting Eyelash and Reflection for Accurate Iris Segmentation , 2003, Int. J. Pattern Recognit. Artif. Intell..

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

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

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

[23]  K.W. Bowyer,et al.  All Iris Code Bits are Not Created Equal , 2007, 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems.

[24]  K.W. Bowyer,et al.  The Best Bits in an Iris Code , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[26]  Patrick J. Flynn,et al.  Image understanding for iris biometrics: A survey , 2008, Comput. Vis. Image Underst..

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