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Proceedings Paper

Automatic detection of non-cosmetic soft contact lenses in ocular images
Author(s): Gizem Erdogan; Arun Ross
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Paper Abstract

Recent research in iris recognition has established the impact of non-cosmetic soft contact lenses on the recognition performance of iris matchers. Researchers in Notre Dame demonstrated an increase in False Reject Rate (FRR) when an iris without a contact lens was compared against the same iris with a transparent soft contact lens. Detecting the presence of a contact lens in ocular images can, therefore, be beneficial to iris recognition systems. This study proposes a method to automatically detect the presence of non-cosmetic soft contact lenses in ocular images of the eye acquired in the Near Infrared (NIR) spectrum. While cosmetic lenses are more easily discernible, the problem of detecting non-cosmetic lenses is substantially difficult and poses a significant challenge to iris researchers. In this work, the lens boundary is detected by traversing a small annular region in the vicinity of the outer boundary of the segmented iris and locating candidate points corresponding to the lens perimeter. Candidate points are identified by examining intensity profiles in the radial direction within the annular region. The proposed detection method is evaluated on two databases: ICE 2005 and MBGC Iris. In the ICE 2005 database, a correct lens detection rate of 72% is achieved with an overall classification accuracy of 76%. In the MBGC Iris database, a correct lens detection rate of 70% is obtained with an overall classification accuracy of 66:8%. To the best of our knowledge, this is one of the earliest work attempting to detect the presence of non-cosmetic soft contact lenses in NIR ocular images. The results of this research suggest the possibility of detecting soft contact lenses in ocular images but highlight the need for further research in this area.

Paper Details

Date Published: 31 May 2013
PDF: 15 pages
Proc. SPIE 8712, Biometric and Surveillance Technology for Human and Activity Identification X, 87120C (31 May 2013); doi: 10.1117/12.2018096
Show Author Affiliations
Gizem Erdogan, West Virginia Univ. (United States)
Arun Ross, Michigan State Univ. (United States)


Published in SPIE Proceedings Vol. 8712:
Biometric and Surveillance Technology for Human and Activity Identification X
Ioannis Kakadiaris; Walter J. Scheirer; Laurence G. Hassebrook, Editor(s)

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