A robust method of complete iris segmentation

In iris recognition, accurate Iris segmentation is the most crucial step. Iris recognition systems are highly affected by the performance of iris segmentation processing. In this paper a robust and efficient method of iris segmentation is proposed. In the proposed scheme, the inner boundary of iris is calculated by finding the pupil center and radius using first derivative of the image. For outer iris boundary, a band is calculated within which iris outer boundary lies. One dimensional signals are picked along radial direction from the determined band in a sequence at different angles to obtain the outer circle of the iris. Points for upper and lower eyelids are found in the same way as the iris outer boundary followed by the statistically fit parabolas to completely localize the iris. Experimental results show that proposed method is very efficient.

[1]  John Daugman,et al.  The importance of being random: statistical principles of iris recognition , 2003, Pattern Recognit..

[2]  Seongwon Cho,et al.  Iris Recognition Using Wavelet Features , 2004, J. VLSI Signal Process..

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

[4]  Jafar M. H. Ali,et al.  An Iris Recognition System to Enhance E-security Environment Based on Wavelet Theory , 2003 .

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

[6]  Yong-zeng Shen,et al.  A New Iris Locating Algorithm , 2006, 16th International Conference on Artificial Reality and Telexistence--Workshops (ICAT'06).

[7]  Muhammad Younus Javed,et al.  Efficient Iris Recognition Method for Human Identification , 2005, WEC.

[8]  Jamal Ahmad Dargham,et al.  Iris recognition using self-organizing neural network , 2002, Student Conference on Research and Development.

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

[10]  Peter W. Hallinan.  Recognizing human eyes , 1991, Optics & Photonics.

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

[12]  Hanqi Zhuang,et al.  Real-time eye feature tracking from a video image sequence using Kalman filter , 1994, Conference Record Southcon.

[13]  Ashok A. Ghatol,et al.  Iris recognition: an emerging biometric technology , 2007 .

[14]  Lijun Yin,et al.  Eye tracking and animation for MPEG-4 coding , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).