Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31198
A Weighted Approach to Unconstrained Iris Recognition

Authors: Yao-Hong Tsai

Abstract:

This paper presents a weighted approach to unconstrained iris recognition. In nowadays, commercial systems are usually characterized by strong acquisition constraints based on the subject’s cooperation. However, it is not always achievable for real scenarios in our daily life. Researchers have been focused on reducing these constraints and maintaining the performance of the system by new techniques at the same time. With large variation in the environment, there are two main improvements to develop the proposed iris recognition system. For solving extremely uneven lighting condition, statistic based illumination normalization is first used on eye region to increase the accuracy of iris feature. The detection of the iris image is based on Adaboost algorithm. Secondly, the weighted approach is designed by Gaussian functions according to the distance to the center of the iris. Furthermore, local binary pattern (LBP) histogram is then applied to texture classification with the weight. Experiment showed that the proposed system provided users a more flexible and feasible way to interact with the verification system through iris recognition.

Keywords: Authentication, iris recognition, adaboost, local binary pattern

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1336404

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1675

References:


[1] 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.
[2] J. G. Daugman, "How iris recognition works,” IEEE Trans. Circuits Systems Video Technol., vol. 14, no. 1, 2004, pp. 21–29.
[3] P. W. Hallinan, "Recognizing human eyes,” Geometric Methods in Computer Vision, vol. 1570, pp. 214–226, 1991.
[4] K. W. Bowyer, K. Hollingsworth, P.J. Flynn, Image understanding for iris biometrics: a survey, Computer Vision Image Understanding, vol. 110, no. 2, 2008.
[5] H. Proença, S. Filipe, R. Santos, J. Oliveira, and L. A. Alexandre, "The UBIRIS.v2: A database of visible wavelength iris images captured on-the-move and at-adistance,” IEEE Trans. Pattern Anal. Machine Intell., vol. 32, 2010, pp. 1529–1535.
[6] J. Daugman, "New methods in iris recognition,” IEEE Trans. Systems, Man, Cybernet., Part B vol. 37, 2007, pp. 1167–1175.
[7] Z. Sun and T. Tan, "Ordinal measures for iris recognition,” IEEE Trans. Pattern Anal. Machine Intell., vol. 31, 2009, pp. 2211–2226.
[8] P. Li and H. Ma, "Iris recognition in non-ideal imaging conditions,” Pattern Recognition Letters, vol. 33, no. 8, 2012, pp. 1012–1018.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] H. Proença, S. Filipe, R. Santos, J. Oliveira, and L. A. Alexandre, "The UBIRIS.v2: A database of visible wavelength iris images captured on-the-move and at-a-distance,” IEEE Trans. Pattern Anal. Machine Intell., vol. 32, 2010, pp. 1529–1535.
[14] D. M. Gavrila , "The visual analysis of human movement: a survey,” Computer Vision and Image Understanding, vol. 73, 1999, pp. 82-98,.
[15] Iris image database. http://socia-lab.di.ubi.pt/
[16] Y. Moses, S. Edelman, and S. Ullamn, "Generalization on Novel Images in Upright and Inverted Faces,” Perception, vol. 25, 1996, pp. 443-461.
[17] P. J. Phillips and Y. Vardi, "Efficient Illumination Normalization of Facial Images,” Pattern Recognition Letters, vol. 17, 1996, pp. 921-927.
[18] P. Viola, M. J. Jonse, "Robust Real-Time Face Detection,” International Journal of Computer Vision, vol. 57, no. 2, 2004, pp. 137-154.
[19] 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.
[20] R. E. Schapire and Y. Singer, "Improved Boosting Algorithms Using Confidence-rated Predictions.” Machine Learning, vol. 37, no. 3, 1999, pp. 297-336.
[21] 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.
[22] L. L. Huang, A. Shimizu, "A multi-expert approach for robust face detection,” Pattern Recognition, vol. 39, 2006, pp. 1695-1703.
[23] 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.