  Presentation attacks are posing major challenges to most of the biometric
modalities. Iris recognition, which is considered as one of the most accurate
biometric modality for person identification, has also been shown to be
vulnerable to advanced presentation attacks such as 3D contact lenses and
textured lens. While in the literature, several presentation attack detection
(PAD) algorithms are presented; a significant limitation is the
generalizability against an unseen database, unseen sensor, and different
imaging environment. To address this challenge, we propose a generalized deep
learning-based PAD network, MVANet, which utilizes multiple representation
layers. It is inspired by the simplicity and success of hybrid algorithm or
fusion of multiple detection networks. The computational complexity is an
essential factor in training deep neural networks; therefore, to reduce the
computational complexity while learning multiple feature representation layers,
a fixed base model has been used. The performance of the proposed network is
demonstrated on multiple databases such as IIITD-WVU MUIPA and IIITD-CLI
databases under cross-database training-testing settings, to assess the
generalizability of the proposed algorithm.
