Iris Recognition using Robust Localization and Nonsubsampled Contourlet Based Features
The conventional iris recognition methods do not perform well for the datasets where the eye image may contain nonideal data such as specular reflection, off-angle view, eyelid, eyelashes and other artifacts. This paper gives contributions for a reliable iris recognition method using a new scale-, shift- and rotation-invariant feature-extraction method in time-frequency and spatial domains. Indeed, a 2-level nonsubsampled contourlet transform (NSCT) is applied on the normalized iris images and a gray level co-occurrence matrix (GLCM) with 3 different orientations is computed on both spatial image and NSCT frequency subbands. Moreover, the effect of the occluded parts is reduced by performing an iris localization algorithm followed by a four regions of interest (ROI) selection. The extracted feature set is transformed and normalized to reduce the effect of extreme values in the feature vector. Next, significant features for iris recognition are selected by a two-step method composed by a filtering stage and wrapper based selection. Finally, the selected feature set is classified using support vector machine (SVM). The proposed iris identification method was tested on the public iris datasets CASIA Ver.1 and CASIA Ver.4-lamp showing a state-of-the-art performance.
KeywordsGray level co-occurrence matrix Iris recognition Nonsubsampled contourlet transform Feature selection
- 1.Flom, L., & Safir, A. (Feb. 3 1987). Iris recognition system. U.S. Patent 4 641 349.Google Scholar
- 4.Wildes, R. P. Iris recognition: An emerging biometric technology. In Proceedings of the IEEE, Sep 1997 (Vol. 85, pp. 1348–1363, Vol. 9). doi: 10.1109/5.628669.
- 5.Ahamed, A., & Bhuiyan, M. I. H. Low Complexity Iris Recognition using Curvelet Transform. In International Conference on Informatics, Electronics & Vision (ICIEV), 2012 (pp. 548–553).Google Scholar
- 16.CASIA Iris Database. http://www.cbsr.ia.ac.cn/english/Databases.asp, 5 May 2013.
- 17.Proenca, H., Filipe, S., Santos, R., Oliveira, J., & Alexandre, L. A. (2010). The UBIRIS.v2: A Database of Visible Wavelength Iris Images Captured On-the-Move and At-a-Distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(8), 1529–1535. doi: 10.1109/Tpami.2009.66.CrossRefGoogle Scholar
- 23.Li, M. Y., Jiang, M. Y., Han, M., & Yang, M. Q. Iris Recognition Based on a Novel Multiresolution Analysis Framework. In 2010 IEEE International Conference on Image Processing, 2010 (pp. 4101-4104). doi: 10.1109/Icip.2010.5652298.
- 25.Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural Features of Image Classification. IEEE Transaction on Systems, Man and Cybernetics, 3(6), 610–621.Google Scholar
- 26.Khalighi, S., Tirdad, P., Pak, F., & Nunes, U. Shift and Rotation Invariant Iris Feature Extraction Based on Non-subsampled Countourlet Transform and GLCM. In In proceeding of International Conference on Pattern Recognition Applications and Methods, Vilamoura, Portugal, 2012. Google Scholar
- 28.Masek, L. (2003). Recognition of Human Iris Patterns for Biometric Identification. The School of Computer Science and Software Engineering the University of Western Australia.Google Scholar
- 30.Do, M. N., & Vetterli, M. Pyramidal directional filter banks and curvelets. In International Conference on Image Processing, 2001 (Vol. II, pp. 158-161).Google Scholar
- 32.Haralick, R., & Shapiro, L. (1992). Computer and Robot Vision (Vol. 1): Addison-Wesley.Google Scholar
- 34.Becq, G., Charbonnier, S., Chapotot, F., Buguet, A., Bourdon, L., & Baconnier, P. (2005). Comparison between five classifiers for automatic scoring of human sleep recordings. Classification and Clustering for Knowledge Discovery, 4, 113–127.Google Scholar
- 38.Whitney, A. W. (1971). A Direct Method of Nonparametric Measurement Selection. IEEE Transactions on Computers, 20(9).Google Scholar
- 41.Canu, S., Grandvalet, Y., Guigue, V., & Rakotomamonjy, A. (2005). SVM and Kernel methods Matlab toolbox.Google Scholar