Iris Segmentation for Non-ideal Images
DOI:
https://doi.org/10.11113/jt.v74.4549Keywords:
Biometrics, iris recognition, non-ideal iris segmentation, optimizationAbstract
Iris recognition has been regarded as one of the most reliable biometric systems over the past years. Previous studies have shown that the performance of iris recognition systems highly dependent on the performance of their segmentation algorithms. Iris segmentation is the process to isolate the iris region from the surrounded structures of the eye image. However, several iris segmentation algorithms have been developed in the literature, but their segmentation and recognition accuracies drastically degrade with non-ideal iris images acquired in less constrained conditions. Thus, it is crucial to develop a new iris segmentation method to improve iris recognition using non-ideal images. Hence, the objective of this paper is an iris segmentation method on the basis of optimization to isolate the iris region from non-ideal iris images such those affected by reflections, blurred boundaries, eyelids occlusion, and gaze-deviation. Experimental results on the off axis/angle West Virginia University (WVU) iris database demonstrated the superiority of the developed method over state-of-the-art iris segmentation methods considered in this paper. The performance of an iris recognition algorithm based on the developed iris segmentation method was observed to be improved. Â
References
Daugman, J. 2004. How Iris Recognition Works. IEEE Transactions on Circuits and Systems for Video Technology. 14(1): 21–30.
Bowyer, K. W., K. P. Hollingsworth, et al. 2008. Image Understanding for Iris Biometrics: A Survey. Computer Vision and Image Understanding. 110(2): 281–307.
Dorairaj, V., N. A. Schmid, et al. 2005. Performance Evaluation of Non-ideal Iris Based Recognition System Implementing Global ICA Encoding. Proceedings of IEEE International Conference on Image Processing (ICIP '05), IEEE.
Proença, H. 2006. Towards Non-cooperative Biometric Iris Recognition. Department of Computer Science. Covilh˜a, Portugal, University of Beira Interior. Ph.D.: 175.
Schuckers, S. A. C., N. A. Schmid, et al. 2007. On Techniques for Angle Compensation in Nonideal Iris Recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics. 37(5): 1176–1190.
Nabti, M. and A. Bouridane 2008. An Effective and Fast Iris Recognition System Based on a Combined Multiscale Feature Extraction Technique. Pattern Recognition. 41(3): 868–879.
Chen, Y., M. Adjouadi, et al. 2010. A Highly Accurate and Computationally Efficient Approach for Unconstrained Iris Segmentation. Image and Vision Computing. 28(2): 261–269.
Jeong, D. S., J. W. Hwang, et al. 2010. A New Iris Segmentation Method for Non-ideal Iris Images. Image and Vision Computing. 28(2): 254–260.
Li, P., X. Liu, et al. 2010. Robust and Accurate Iris Segmentation in Very Noisy Iris Images. Image and Vision Computing. 28(2): 246–253.
Tan, T., Z. He, et al. 2010. Efficient and Robust Segmentation of Noisy Iris Images for Non-cooperative Iris Recognition. Image and Vision Computing. 28(2): 223–230.
Daugman, J. 2007. New Methods in Iris Recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics. 37(5): 1167–1175.
Vatsa, M., R. Singh, et al. 2008. Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics. 38(4): 1021–1035.
Shah, S. and A. Ross 2009. Iris Segmentation Using Geodesic Active Contours. IEEE Transactions on Information Forensics and Security. 4(4): 824–836.
Roy, K., P. Bhattacharya, et al. 2011. Iris Segmentation Using Variational Level Set Method. Optics and Lasers in Engineering. 49(4): 578–588.
Radman, A., K. Jumari, et al. 2013. Fast and Reliable Iris Segmentation Algorithm. IET Image Processing. 7(1): 42–49.
Puhan, N. B., N. Sudha, et al. 2011. Efficient Segmentation Technique for Noisy Frontal View Iris Images Using Fourier Spectral Density. Signal, Image and Video Processing. 5(1): 105–119.
Radman, A., K. Jumari, et al. 2014. Iris Segmentation in Visible Wavelength Images Using Circular Gabor Filters and Optimization. Arabian Journal for Science and Engineering. 1–11.
WVU. 2004. Off Axis/Angle Iris Dataset, Release 1. Retrieved 12 September 2013, from http://www.citer.wvu.edu/off__axis_angle_iris_dataset_collection_release1.
Libor, M. and K. Peter. 2003. MATLAB Source Code for a Biometric Identification System Based on Iris Patterns. Retrieved 27 Feburary 2014, from http://www.csse.uwa.edu.au/~pk/studentprojects/libor/sourcecode.html.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
