Paper
15 November 2007 Novel algorithm for iris localization
Yunxin Wang, Tiegen Liu, Li Liu
Author Affiliations +
Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 67880L (2007) https://doi.org/10.1117/12.747203
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China
Abstract
With the emerging security demands, biometric identification technology has attracted more and more attention in recent years, and iris recognition is one of the most reliable biometric technologies. Iris localization is a crucial part in the iris recognition, which is quite time-consuming and easily disturbed by various noises, especially the eyelashes. A novel iris localization method is proposed in this paper. In the location of inner iris boundary, the gray curves of a row and a column with the pupil edge are used to estimate the coarse center and radius of pupil, which can reject the eyelash noises. The experiments show this coarse location method has better accuracy and speed than the common gray projection. Edge points of pupil are extracted by a gradient operator and fitted as the iris inner boundary. In the location of outer iris boundary, the image binarization is use to mark most noises, and then the outer iris boundary is extracted by integro-differential operator from the coarseness to fine. Performance experiments have been done, and the results show that about 0.175 second at speed and 99.5% at precision are reached by developed algorithm. In comparison with other classical methods, this algorithm has faster speed and better robustness.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yunxin Wang, Tiegen Liu, and Li Liu "Novel algorithm for iris localization", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67880L (15 November 2007); https://doi.org/10.1117/12.747203
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KEYWORDS
Iris recognition

Eye

Algorithm development

Biometrics

Databases

Edge detection

Binary data

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