Real-time iris segmentation based on image morphology

ACM International Conference Proceeding Series

Volumn , Issue , 2011, Pages 335-338

  Bakshi, Sambit ^a   Mehrotra, Hunny ^a   Majhi, Banshidhar ^b  

^a NATIONAL INSTITUTE OF TECHNOLOGY   (India)

^b KING KHALID UNIVERSITY   (Saudi Arabia)

Author keywords

Adaptive threshold; Connected components; Iris segmentation; Spectrum image and circular hough transform

Indexed keywords

ADAPTIVE THRESHOLDS; BIOMETRIC SYSTEMS; CONNECTED COMPONENT; IMAGE MORPHOLOGY; IRIS IMAGES; IRIS SEGMENTATION; LOCALISATION; SPECTRUM IMAGES; SPECULAR REFLECTIONS;

BIOMETRICS; HOUGH TRANSFORMS; MATHEMATICAL TRANSFORMATIONS; REAL TIME SYSTEMS;

IMAGE SEGMENTATION;

EID: 79952913767     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1947940.1948010     Document Type: Conference Paper

References (8)

1. J. Daugman. The importance of being random: statistical principles of iris recognition. Pattern Recognition, 36(2):279 - 291, 2003.
2. G. Xu, Z.F. Zhang, and Y.D. Ma. Automatic iris segmentation based on local areas. In International Conference on Pattern Recognition, volume 4, pages 505-508. IEEE Computer Society, 2006. (Pubitemid 46553469)
3. Q. Tian, Q. Pan, Y. Cheng, and Q. Gao. Fast algorithm and application of hough transform in iris segmentation. In International Conference on Machine Learning and Cybernetics, volume 7, pages 3977-3980, 2004.
4. B. Lipinski. Iris recognition: Detecting the pupil. Connexions website, 2004. http://cnx.org/content/m12487/1.4/.
5. L. Ma, T. Tan, Y. Wang, and D. Zhang. Efficient iris recognition by characterizing key local variations. IEEE Transactions on Image Processing, 13(6):739-750, 2004.
6. BATH University Database. http://www.bath.ac.uk/elec-eng/research/sipg/ irisweb.
7. CASIA Database. http://www.cbsr.ia.ac.cn/english/IrisDatabase.asp.
8. R.P. Wildes. Iris recognition: an emerging biometric technology. Proceedings of the IEEE, 85(9):1348-1363, 1997. (Pubitemid 127745626)