A robust iris segmentation with fuzzy supports

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

Volumn 6443 LNCS, Issue PART 1, 2010, Pages 532-539

  Teo, C C ^a   Neo, H F ^a   Michael, G K O ^a   Tee, C ^a   Sim, K S ^a  

^a MULTIMEDIA UNIVERSITY   (Malaysia)

Author keywords

Biometrics; Fuzzy; Iris Segmentation; MMU2; Pupil

Indexed keywords

CONTACT LESS; FUZZY; FUZZY SUPPORT; IRIS IMAGES; IRIS RECOGNITION; IRIS SEGMENTATION; IRIS SEGMENTATION METHOD; MMU2; PUPIL;

DATA PROCESSING; ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

BIOMETRICS;

EID: 78650216766     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-17537-4_65     Document Type: Conference Paper

References (20)

1. Cui, J., Wang, Y., Tan, T., Ma, L., Sun, Z.: A Fast and Robust Iris Localization Method Based on Texture Segmentation. In: SPIE Defense and Security Symposium (2004)
2. Tisse, C., Martin, L., Torres, L., Robert, M.: Person Identification Technique using Human Iris Recognition. In: Proc. Vision Interface, pp. 294-299 (2002)
3. Boles, W., Boashash, B.: A Human Identification Technique Using Images of the Iris and Wavelet Transform. IEEE Trans. Signal Processing 46(4), 1185-1188 (1998)
4. He, Z., Tan, T., Sun, Z., Qiu, X.: Toward Accurate and Fast Iris Segmentation for Iris Biometrics. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 31(9), 1670-1684 (2009)
5. Daugman, J.: How Iris Recognition Works. IEEE Trans, Circuits and Systems for Video Technology 14(1), 21-30 (2004)
6. Wildes, R.: Iris Recognition: An Emerging Biometric Technology. Proc. of IEEE 85(9), 1348-1363 (1997)
7. CASIA Iris Image Database V-3.0: (2010), http://www.cbsr.ia.ac.cn/ IrisDatabase.htm
8. Ma, L., Tan, T., Wang, Y., Zhang, D.: Efficient Iris Recognition by characterizing Key Local Variations. IEEE Trans. IP 13(6), 739-750 (2004)
9. Proenca, H., Alexandre, L.A.: UBIRIS: A noisy iris image database. Proc. ICAIP 1, 970-977 (2005)
10. Proenca, H., Alexandre, L.A.: Iris Segmentation Methodology for Non-Cooperative Recognition. IEE Proc. VISP 153, 199-205 (2006)
11. Carlos, A.C.M.B., Tsang, I.R., George, D.C.C.: A Combined Pulling & Pushing and Active Contour Method for Pupil Segmentation. In: Proc. ICASSP, pp. 850-853 (2010)
12. Jonathan, M.S.: A New Method of Pupil Identification. IEEE Potential, 15-20 (2010)
13. MMU1 Iris Database (2010), http://pesona.mmu.edu.my/~ccteo/ MMUirisdatabase.rar
14. MMU2 Iris Database (2010), http://pesona.mmu.edu.my/~ccteo/ MMU2irisdatabase.rar
15. CASIA Iris Image Database, V-1.0 (2010), http://www.sinobiometrics.com
16. Mallat, S.G.: A Theory For Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Trans. Pattern Analy. Mach. Intell. 11(7), 674-693 (1989)
17. Lizarazo, I., Elsner, P.: Fuzzy regions for handling uncertainly in remote sensing image segmentation. In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L. (eds.) ICCSA 2008, Part I. LNCS, vol. 5072, pp. 724-739. Springer, Heidelberg (2008)
18. Milindkumar, V.S., Ladhake, S.A., Prashant, R.D.: Fuzzy system for color image enhancement. World Academy of Science, Engineering and Technology, 311-316 (2008)
19. Fan, J., Yau David, K.Y., Elmagamid, A.K., Aref, W.G.: Automatic image segmentation by integrating color edge extraction and seeded region growing. IEEE Trans. Image Processing 10, 1454-1466 (2001)
20. Zadeh, L.: Fuzzy Sets. Information and control (8), 338-353 (1965)