Efficient Feature Fusion for Noise Iris in Unconstrained Environment
Authors: Yao-Hong Tsai
This paper presents an efficient fusion algorithm for iris images to generate stable feature for recognition in unconstrained environment. Recently, iris recognition systems are focused on real scenarios in our daily life without the subject’s cooperation. Under large variation in the environment, the objective of this paper is to combine information from multiple images of the same iris. The result of image fusion is a new image which is more stable for further iris recognition than each original noise iris image. A wavelet-based approach for multi-resolution image fusion is applied in the fusion process. The detection of the iris image is based on Adaboost algorithm and then local binary pattern (LBP) histogram is then applied to texture classification with the weighting scheme. Experiment showed that the generated features from the proposed fusion algorithm can improve the performance for verification system through iris recognition.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1099571Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1825
 J. G. Daugman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Trans. Pattern Anal. Machine Int., vol. 15, no. 11, 1993, pp. 1148–1161.
 J. G. Daugman, “How iris recognition works,” IEEE Trans. Circuits Systems Video Technol., vol. 14, no. 1, 2004, pp. 21–29.
 P. W. Hallinan, “Recognizing human eyes,” Geometric Methods in Computer Vision, vol. 1570, pp. 214–226, 1991.
 K. W. Bowyer, K. Hollingsworth, P.J. Flynn, Image understanding for iris biometrics: a survey, Computer Vision Image Understanding, vol. 110, no. 2, 2008.
 H. Proença, S. Filipe, R. Santos, J. Oliveira, and L. A. Alexandre, “The UBIRIS.v2: A database of visible wavelength iris images captured onthe- move and at-adistance,” IEEE Trans. Pattern Anal. Machine Intell., vol. 32, 2010, pp. 1529–1535.
 J. Daugman, “New methods in iris recognition,” IEEE Trans. Systems, Man, Cybernet., Part B vol. 37, 2007, pp. 1167–1175.
 Z. Sun and T. Tan, “Ordinal measures for iris recognition,” IEEE Trans. Pattern Anal. Machine Intell., vol. 31, 2009, pp. 2211–2226.
 P. Li and H. Ma, “Iris recognition in non-ideal imaging conditions,” Pattern Recognition Letters, vol. 33, no. 8, 2012, pp. 1012–1018.
 H. Proença, L.A. Alexandre, Iris segmentation methodology for noncooperative recognition, IEEE Proceedings of the Vision, Image and Signal Processing, vol. 153, no. 2, 2006, pp. 199–205.
 J. G. Daugman, New methods in iris recognition, IEEE Transactions on System, Man, and Cybernetics–Part B: Cybernetics, vol. 37, no. 5, 2007, pp. 1167–1175.
 K. W. Bowyer, K. Hollingsworth, and P. J. Flynn, “Image understanding for iris biometrics: A survey,” Computer Vision and Image Understanding, vol. 110, 2008, pp. 281–307.
 H. Proença, L.A. Alexandre, The NICE.I: noisy iris challenge evaluation – part I, in: Proceedings of the of First International Conference on Biometrics: Theory, Applications, and Systems, 2007, pp. 1–4.
 H. Proença, S. Filipe, R. Santos, J. Oliveira, and L. A. Alexandre, “The UBIRIS.v2: A database of visible wavelength iris images captured onthe- move and at-a-distance,” IEEE Trans. Pattern Anal. Machine Intell., vol. 32, 2010, pp. 1529–1535.
 Y. H. Tsai, “A Weighted Approach to Unconstrained Iris Recognition,” in Proc. International Conference on Computer Science and Intelligent Systems, London, UK, 2014.
 Iris image database. http://socia-lab.di.ubi.pt/
 Y. Moses, S. Edelman, and S. Ullamn, “Generalization on Novel Images in Upright and Inverted Faces,” Perception, vol. 25, 1996, pp. 443-461.
 P. J. Phillips and Y. Vardi, “Efficient Illumination Normalization of Facial Images,” Pattern Recognition Letters, vol. 17, 1996, pp. 921-927.
 P. Viola, M. J. Jonse, “Robust Real-Time Face Detection,” International Journal of Computer Vision, vol. 57, no. 2, 2004, pp. 137-154.
 Y. Freund and R. E. Schapire, “Experiments with a New Boosting Aalgorithm,” in Proc. 13th International Conference on Machine Learning, Bari, Italy, 1996, pp. 148-156.
 R. E. Schapire and Y. Singer, “Improved Boosting Algorithms Using Confidence-rated Predictions.” Machine Learning, vol. 37, no. 3, 1999, pp. 297-336.
 J. Friedman, T. Hastie, and R. Tibshirani, “Additive Logistic Regression: a Statistical View of Boosting,” The Annals of Statistics, vol.28, no.2, 2000, pp. 337-407.
 L. L. Huang, A. Shimizu, “A multi-expert approach for robust face detection,” Pattern Recognition, vol. 39, 2006, pp. 1695-1703.
 T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. on PAMI, vol. 24, no. 7, 2002, pp. 971–987.
 V. Vezhnevets, V. Sazonov and A. Andreeva, “A Survey on Pixel-Based Skin Color Detection Techniques” Proc. of Graphicon conference, 2003.
 G. Pajares and J. M. de la Cruz” A wavelet-based image fusion tutorial”, Pattern Recognition Volume 37, Issue 9, September (2004), pp. 1855- 1872