We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Deep Learning-Based Iris Segmentation for Iris Recognition in Visible Light Environment.
- Authors
Arsalan, Muhammad; Hyung Gil Hong; Naqvi, Rizwan Ali; Min Beom Lee; Min Cheol Kim; Dong Seop Kim; Chan Sik Kim; Kang Ryoung Park
- Abstract
Existing iris recognition systems are heavily dependent on specific conditions, such as the distance of image acquisition and the stop-and-stare environment, which require significant user cooperation. In environments where user cooperation is not guaranteed, prevailing segmentation schemes of the iris region are confronted with many problems, such as heavy occlusion of eyelashes, invalid off-axis rotations, motion blurs, and non-regular reflections in the eye area. In addition, iris recognition based on visible light environment has been investigated to avoid the use of additional near-infrared (NIR) light camera and NIR illuminator, which increased the difficulty of segmenting the iris region accurately owing to the environmental noise of visible light. To address these issues; this study proposes a two-stage iris segmentation scheme based on convolutional neural network (CNN); which is capable of accurate iris segmentation in severely noisy environments of iris recognition by visible light camera sensor. In the experiment; the noisy iris challenge evaluation part-II (NICE-II) training database (selected from the UBIRIS.v2 database) and mobile iris challenge evaluation (MICHE) dataset were used. Experimental results showed that our method outperformed the existing segmentation methods.
- Subjects
IRIS recognition; BIOMETRIC eye scanning systems; INFRARED cameras; NEAR infrared radiation; BIOMETRIC identification
- Publication
Symmetry (20738994), 2017, Vol 9, Issue 11, p263
- ISSN
2073-8994
- Publication type
Article
- DOI
10.3390/sym9110263