Multi-stage visible wavelength and near infrared iris segmentation framework

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7325 LNCS, Issue PART 2, 2012, Pages 1-10

  Uhl, Andreas ^a   Wild, Peter ^a  

^a UNIVERSITY OF SALZBURG   (Austria)

Author keywords

Iris biometrics; preprocessing; segmentation

Indexed keywords

CENTER DETECTION; CLASSICAL APPROACH; EXHAUSTIVE SEARCH; HUMAN EYE; INTEGRO DIFFERENTIAL OPERATOR; IRIS BIOMETRICS; IRIS SEGMENTATION; ITERATIVE APPROACH; LIGHT SPECTRUM; MODULAR DESIGNS; MULTI-STAGE; NEAR INFRARED; PARAMETER-TUNING; PREPROCESSING; SEGMENTATION PROCESS; VISIBLE WAVELENGTHS;

BIOMETRICS; HOUGH TRANSFORMS; IMAGE ANALYSIS; IMAGE SEGMENTATION;

INFRARED DEVICES;

EID: 84864144117     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-31298-4_1     Document Type: Conference Paper

References (17)

1. Abhyankar, A., Schuckers, S.: Active shape models for effective iris segmentation. In: Proc. of SPIE, p. 62020H (2006)
2. Bowyer, K.W., Hollingsworth, K., Flynn, P.J.: Image understanding for iris biometrics: A survey. Computer Vision and Image Understanding 110(2), 281-307 (2008)
3. Cauchie, J., Fiolet, V., Villers, D.: Optimization of an hough transform algorithm for the search of a center. Pattern Recognition 41(2), 567-574 (2008)
4. Chen, Y., Adjouadi, M., Han, C., Wang, J., Barreto, A., Rishe, N., Andrian, J.: A highly accurate and computationally efficient approach for unconstrained iris segmentation. Image Vision Computing 28, 261-269 (2010)
5. Daugman, J.: How iris recognition works. IEEE Trans. on Circiuts and Systems for Video Technology 14(1), 21-30 (2004)
6. Daugman, J.: New methods in iris recognition. IEEE Trans. on Systems, Man, and Cybenetics Part B 37(5), 1167-1175 (2007)
7. He, Z., Tan, T., Sun, Z.: Iris localization via pulling and pushing. In: Proc. of Int'l Conf. on Pattern Recognition (ICPR), pp. 366-369 (2006)
8. Labati, R.D., Piuri, V., Scotti, F.: Agent-based image iris segmentation and multipleviews boundary refining. In: Proc. of IEEE Int'l Conf. on Biometrics: Theory, Applications and Systems (BTAS), pp. 204-210 (2009)
9. Li, P., Liu, X.: An incremental method for accurate iris segmentation. In: Proc. of Int'l Conf. on Pattern Recognition (ICPR), pp. 1-4 (2008)
10. Luengo-Oroz, M., Faure, E., Angulo, J.: Robust iris segmentation on uncalibrated noisy images using mathematical morphology. Image Vision Computing 28, 278-284 (2010)
11. Ma, L., Tan, T., Wang, Y., Zhang, D.: Efficient iris recognition by characterizing key local variations. IEEE Trans. on Image Processing 13(6), 739-750 (2004)
12. Proenca, H.: Iris Recognition: AMethod to Segment VisibleWavelength Iris Images Acquired On-the-Move and At-a-Distance. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Porikli, F., Peters, J., Klosowski, J., Arns, L., Chun, Y.K., Rhyne, T.-M., Monroe, L. (eds.) ISVC 2008, Part I. LNCS, vol. 5358, pp. 731-742. Springer, Heidelberg (2008)
13. Proenca, H., Alexandre, L.A.: Iris recognition: Analysis of the error rates regarding the accuracy of the segmentation stage. Image and Vision Computing 28(1), 202-206 (2010)
14. Reza, A.M.: Realization of the contrast limited adaptive histogram equalization (clahe) for real-time image enhancement. J. VLSI Signal Process. Syst. 38(1), 35-44 (2004)
15. Schuckers, S., Schmid, N., Abhyankar, A., Dorairaj, V., Boyce, C., Hornak, L.: On techniques for angle compensation in nonideal iris recognition. IEEE Trans. on Systems, Man, and Cybenetics Part B 37(5), 1176-1190 (2007)
16. Uhl, A., Wild, P.: Weighted adaptive hough and ellipsopolar transforms for real-time iris segmentation. In: Proc. Int'l Conf. on Biometrics, ICB, pp. 1-8 (2012)
17. Wildes, R.P.: Iris recognition: an emerging biometric technology. Proc. of the IEEE 85, 1348-1363 (1997)