A robust segmentation approach to iris recognition based on video

Proceedings - Applied Imagery Pattern Recognition Workshop

Volumn , Issue , 2008, Pages

  Chen, Yu ^a   Wang, Jin ^a   Han, Changan ^a   Wang, Lu ^a   Adjouadi, Malek ^a  

^a Florida International University   (United States)

Author keywords

Hough transform; Image processing; Iris recognition; NIR, iris segmentation; Noise, data overlap

Indexed keywords

EYE IMAGES; IRIS ACQUISITION; IRIS IMAGES; IRIS RECOGNITION; IRIS SEGMENTATION; KEY PROBLEMS; MOVING SUBJECT; NEAR INFRA RED; NEW STRATEGY; NIR, IRIS SEGMENTATION; NOISE EFFECTS; NOISE SOURCE; NOISE, DATA OVERLAP; ROBUST SEGMENTATION; TIME EFFICIENCIES; VIDEO FRAME;

BIOMETRICS; HOUGH TRANSFORMS; IMAGE QUALITY; IMAGING SYSTEMS; INFRARED DEVICES;

VIDEO CAMERAS;

EID: 69949109605     PISSN: 15505219     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/AIPR.2008.4906441     Document Type: Conference Paper

References (13)

1. CASIA iris image database, http://www.sinobiometrics.com/casiairis.htm.
2. Multiple Biometric Grand Challenge (MBGC) Database http://face.nist.gov/ mbgc/
3. W. Kong and D. Zhang, "Detecting eyelash and reflection for accurate iris segmentation. International Journal of Pattern Recognition and Artificial Intelligence", 17(6), pp. 1025-1034, 2003.
4. Mansfield, A.J. and Wayman, J.L. (2002), "Best practices in testing and reporting performance of biometric devices", UK Government Biometrics Working Group, http://3003.www.cesg.uk.gov/site/ast/biometrics/media/ BestPractive.pdf
5. J. Daugman, "High Confidence Visual Recognition of Persons by a Test of Statistical Independence", Trans. Pattern Analysis and Machine Intelligence, Vol.15, No 11, pp. 1148-1161, 1993.
6. J.R. Matey, O. Naroditsky, K. Hanna, R. Kolczynski, D. LoIacono, S. Mangru, M. Tinker, T. Zappia, W.Y. Zhao, "Iris on the Move : acquisition of images for iris recognition in less constrained environments", Proceedings of the IEEE, 94 (11), pp. 1936-1946, 2006.
7. John Daugman, "How iris recognition works", IEEE Trans. Circ. Syst. Video Technol. 14 (1), pp. 21-30, 2004.
8. J.Kittler, "On the accuracy of the Sobel edge detector", Image and Vision Computing, 1 (1), pp. 37-42, 1983.
9. X. Liu, K. W. Bowyer, and P. J. Flynn. Experimental evaluation of iris recognition, Proceedings of Face Recognition Grand Challenge Workshop, 2005, FRGC 05 III: 158-158.
10. Elaine Newton, P. Jonathan Phillips, "Meta-analysis of third party evaluations of iris recognition", in: Biometrics: Theory, Applications, and Systems, Sept 2007, 1-4.
11. Hugo Proença, Luís Alexandre, Toward noncooperative iris recognition:a classification approach using multiple signatures, IEEE Trans. Pattern Anal. Mach. Intell. 29 (4) pp. 607-612, 2007.
12. J. Daugman. The importance of being random: Statistical principles of iris recognition. Pattern Recognition, 36(2):279-291, 2003.
13. J. Woodward, N. M. Orlans, P. T. Higgins, Biometrics, McGraw-Hill, Berkeley, CA, 2002.