The widespread use of smartphones has spurred the research in mobile iris devices. Due to their convenience, these mobile devices are also utilized in unconstrained outdoor scenarios. This has necessitated the development of reliable iris recognition algorithms for such uncontrolled environment. At the same time, iris presentation attacks pose a major challenge to current iris recognition systems. It has been shown that print attacks and textured contact lens may significantly degrade the iris recognition performance. Motivated by these factors, we present a novel Mobile Uncontrolled Iris Presentation Attack Database (MUIPAD). The database contains more than 10,000 iris images that are acquired with and without textured contact lenses in indoor and outdoor environments using a mobile sensor. We also investigate the efficacy of textured contact lens in identity impersonation and obfuscation. Moreover, we demonstrate the effectiveness of deep learning based features for iris presentation attack detection on the proposed database.
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Synthetic iris presentation attack using iDCGAN
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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- NSF-PAR ID:
- 10053783
- Date Published:
- Journal Name:
- International Joint Conference on Biometrics (IJCB)
- Page Range / eLocation ID:
- 674 to 680
- 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.2017.8272756
