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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Unconstrained visible spectrum iris with textured contact lens variations: Database and benchmarking
Iris recognition in visible spectrum has developed into an
active area of research. This has elevated the importance of
efficient presentation attack detection algorithms, particularly
in security based critical applications. In this paper,
we present the first detailed analysis of the effect of contact
lenses on iris recognition in visible spectrum. We introduce
the first contact lens database in visible spectrum, Unconstrained
Visible Contact Lens Iris (UVCLI) Database,
containing samples from 70 classes with subjects wearing
textured contact lenses in indoor and outdoor environments
across multiple sessions. We observe that textured contact
lenses degrade the visible spectrum iris recognition performance
by over 25% and thus, may be utilized intentionally
or unintentionally to attack existing iris recognition
systems. Next, three iris presentation attack detection
(PAD) algorithms are evaluated on the proposed database
and highest PAD accuracy of 82.85% is observed. This illustrates
that there is a significant scope of improvement
in developing efficient PAD algorithms for detection of textured
contact lenses in unconstrained visible spectrum iris
images.
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- NSF-PAR ID:
- 10053785
- Date Published:
- Journal Name:
- International Joint Conference on Biometrics (IJCB)
- Page Range / eLocation ID:
- 574 to 580
- 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.2017.8272744
