GHCLNet: A Generalized Hierarchically tuned Contact Lens detection Network
This addresses a critical security issue in biometric authentication systems by improving reliability against spoofing attacks, though it is incremental as it builds on existing ResNet-50 architecture.
The paper tackles the problem of contact lens detection in iris biometrics to prevent spoofing, proposing GHCLNet which classifies iris images into no lens, soft lens, and cosmetic lens without pre-processing, and reports outperforming state-of-the-art methods on multiple datasets.
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.