Robust Detection of Textured Contact Lenses in Iris Recognition Using BSIF

• Published in: IEEE access 2015, Vol.3, p.1672-1683
• Main Authors: Doyle, James S., Bowyer, Kevin W.
• Format: Article
• Language: English
• Subjects: Algorithms; Biometric recognition systems; Classification algorithms; Contact lenses; Detection algorithms; Detectors; Eyes; Image acquisition; Image classification; Image processing; Image segmentation; Iris recognition; Lenses; Object recognition; Robustness; Sensors
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2015.2477470

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.