Reliability and accuracy of iris biometric modality has
prompted its large-scale deployment for critical applications
such as border control and national ID projects. The
extensive growth of iris recognition systems has raised apprehensions
about susceptibility of these systems to various
attacks. In the past, researchers have examined the impact
of various iris presentation attacks such as textured contact
lenses and print attacks. In this research, we present a novel
presentation attack using deep learning based synthetic iris
generation. Utilizing the generative capability of deep convolutional
generative adversarial networks and iris quality
metrics, we propose a new framework, named as iDCGAN
(iris deep convolutional generative adversarial network) for
generating realistic appearing synthetic iris images. We
demonstrate the effect of these synthetically generated iris
images as presentation attack on iris recognition by using
a commercial system. The state-of-the-art presentation attack
detection framework, DESIST is utilized to analyze if it
can discriminate these synthetically generated iris images
from real images. The experimental results illustrate that
mitigating the proposed synthetic presentation attack is of
paramount importance.
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LivDet iris 2017 — Iris liveness detection competition 2017
Presentation attacks such as using a contact lens with
a printed pattern or printouts of an iris can be utilized to
bypass a biometric security system. The first international
iris liveness competition was launched in 2013 in order
to assess the performance of presentation attack detection
(PAD) algorithms, with a second competition in 2015. This
paper presents results of the third competition, LivDet-Iris
2017. Three software-based approaches to Presentation Attack
Detection were submitted. Four datasets of live and
spoof images were tested with an additional cross-sensor
test. New datasets and novel situations of data have resulted
in this competition being of a higher difficulty than
previous competitions. Anonymous received the best results
with a rate of rejected live samples of 3.36% and rate of accepted
spoof samples of 14.71%. The results show that even
with advances, printed iris attacks as well as patterned contacts
lenses are still difficult for software-based systems to
detect. Printed iris images were easier to be differentiated
from live images in comparison to patterned contact lenses
as was also seen in previous competitions.
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- NSF-PAR ID:
- 10053775
- Date Published:
- Journal Name:
- International Joint Conference on Biometrics (IJCB)
- Page Range / eLocation ID:
- 733 to 741
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BTAS.2017.8272763
