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A robust preprocessing algorithm for iris segmentation from low contrast eye images

Badejo, J.A. and Atayero, A.A. and Ibiyemi, T.S. (2017) A robust preprocessing algorithm for iris segmentation from low contrast eye images. In: IEEE co-sponsored Future Technologies Conference (FTC’16), 6 – 7 December 2016, Hyatt Fisherman's Wharf, San Francisco, CA, USA.

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Abstract

Iris recognition systems offer highly accurate personal identification both on small and very-large scale systems needed in government, forensic and commercial applications. The automatic segmentation of a noise-free iris region is imperative for optimal performance of the system. However, image characteristics such as brightness and contrast, the differing levels of pigmentation, occlusion by eyelashes and/or eyelids, coupled with varying sensor and environmental conditions, makes iris segmentation a huge and difficult task. This paper proposes an image pre-processing algorithm for robust iris segmentation of low contrast images, aimed at reducing mis-localization errors of basic curve-fitting algorithms. Similar to face detection, the algorithm performs iris detection with a k-NN classifier trained with features extracted by a rotation-invariant texture descriptor based on the co-occurrence of local binary patterns. The integration of the proposed algorithm into an existing open-source iris segmentation module offered a 40 improvement in execution time; a segmentation accuracy of 92 was also recorded over 1,898 low contrast eye images acquired from African subjects. The low contrast eye images were acquired to support diversity in iris recognition. © 2016 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: cited By 3; Conference of 2016 Future Technologies Conference, FTC 2016 ; Conference Date: 6 December 2016 Through 7 December 2016; Conference Code:126003
Uncontrolled Keywords: Biometrics; Curve fitting; Face recognition; Image acquisition; Image processing; Large scale systems; Nearest neighbor search; Pattern recognition, Iris Dataset; Iris detection; Iris segmentation; Local binary patterns; Texture descriptor, Image segmentation
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: UNSPECIFIED
Depositing User: Dr. Joke Badejo
Date Deposited: 17 Sep 2018 10:14
Last Modified: 17 Sep 2018 10:14
URI: http://eprints.covenantuniversity.edu.ng/id/eprint/11671

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