DocumentCode :
3605738
Title :
Robust Detection of Textured Contact Lenses in Iris Recognition Using BSIF
Author :
Doyle, James S. ; Bowyer, Kevin W.
Author_Institution :
Dept. of Comput. Sci. & Eng., Univ. of Notre Dame, Notre Dame, IN, USA
Volume :
3
fYear :
2015
fDate :
7/7/1905 12:00:00 AM
Firstpage :
1672
Lastpage :
1683
Abstract :
This paper considers three issues that arise in creating an algorithm for the robust detection of textured contact lenses in iris recognition images. The first issue is whether the accurate segmentation of the iris region is required in order to achieve the accurate detection of textured contact lenses. Our experimental results suggest that accurate iris segmentation is not required. The second issue is whether an algorithm trained on the images acquired from one sensor will well generalize to the images acquired from a different sensor. Our results suggest that using a novel iris sensor can significantly degrade the correct classification rate of a detection algorithm trained with the images from a different sensor. The third issue is how well a detector generalizes to a brand of textured contact lenses, not seen in the training data. This paper shows that a novel textured lens type may have a significant impact on the performance of textured lens detection.
Keywords :
image classification; image segmentation; image texture; iris recognition; object detection; BSIF; classification rate; iris recognition; iris region segmentation; iris sensor; robust textured contact lenses detection; Classification algorithms; Contact lenses; Detection algorithms; Detectors; Eyes; Image processing; Image segmentation; Iris recognition; Lenses; Biometrics; image classification; image processing; image texture analysis; machine learning;
fLanguage :
English
Journal_Title :
Access, IEEE
Publisher :
ieee
ISSN :
2169-3536
Type :
jour
DOI :
10.1109/ACCESS.2015.2477470
Filename :
7264974
Link To Document :
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