  Iris serves as one of the best biometric modality owing to its complex,
unique and stable structure. However, it can still be spoofed using fabricated
eyeballs and contact lens. Accurate identification of contact lens is must for
reliable performance of any biometric authentication system based on this
modality. In this paper, we present a novel approach for detecting contact lens
using a Generalized Hierarchically tuned Contact Lens detection Network
(GHCLNet) . We have proposed hierarchical architecture for three class oculus
classification namely: no lens, soft lens and cosmetic lens. Our network
architecture is inspired by ResNet-50 model. This network works on raw input
iris images without any pre-processing and segmentation requirement and this is
one of its prodigious strength. We have performed extensive experimentation on
two publicly available data-sets namely: 1)IIIT-D 2)ND and on IIT-K data-set
(not publicly available) to ensure the generalizability of our network. The
proposed architecture results are quite promising and outperforms the available
state-of-the-art lens detection algorithms.
