A New Iris Region Segmentation Method

Iris edge detection is a key step for iris boundary localization in iris recognition system, because there are many edge points caused by influences in iris image such as eyelashes, eyelid and light spots, it causes that edge detection and localization are generally computationally expensive. In order to improve the speed of iris boundaries localization, a new edge detection method for iris images is presented in this article. This method use morphological operators and fuzzy membership functions to select edge points by sharpening edge gradient magnitude. The results of simulation show that the method is effectiveness for decreasing invalid iris edge points and improving iris boundaries localization speed.

[1]  Kaoru Arakawa,et al.  Median filter based on fuzzy rules and its application to image restoration , 1996, Fuzzy Sets Syst..

[2]  Yuang-Cheh Hsueh,et al.  Fuzzy logic approach for removing noise , 1995, Neurocomputing.

[3]  Giovanni Ramponi,et al.  A noise smoother using cascaded FIRE filters , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[4]  In-So Kweon,et al.  Automatic edge detection using 3 x 3 ideal binary pixel patterns and fuzzy-based edge thresholding , 2004, Pattern Recognit. Lett..

[5]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Giovanni Ramponi,et al.  Fuzzy operator for sharpening of noisy images , 1992 .

[7]  Yutaka Murata,et al.  Fuzzy filters for image smoothing , 1994, Electronic Imaging.

[8]  Gonzalo R. Arce,et al.  Multidimensional Morphological Edge Detection , 1987, Other Conferences.

[9]  F. Russo.  A new class of fuzzy operators for image processing: design and implementation , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[10]  R. Haralick,et al.  Morphologic edge detection , 1986, IEEE J. Robotics Autom..

[11]  George K. Knopf,et al.  Fuzzy uncertainty measures in image processing , 1994, J. Electronic Imaging.

[12]  F. Russo,et al.  A user-friendly research tool for image processing with fuzzy rules , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[13]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  Raghu Krishnapuram,et al.  Morphological methods for detection and classification of edges in range images , 1992, Journal of Mathematical Imaging and Vision.

[15]  Giovanni Ramponi,et al.  Nonlinear fuzzy operators for image processing , 1994, Signal Process..

[16]  F. Russo,et al.  A fuzzy filter for images corrupted by impulse noise , 1996, IEEE Signal Processing Letters.

[17]  Yrjö Neuvo,et al.  Robust edge detector based on morphological filters , 1991, China., 1991 International Conference on Circuits and Systems.

[18]  Josef Kittler,et al.  Optimal Edge Detectors for Ramp Edges , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

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