  An iris presentation attack detection (IPAD) is essential for securing
personal identity is widely used iris recognition systems. However, the
existing IPAD algorithms do not generalize well to unseen and cross-domain
scenarios because of capture in unconstrained environments and high visual
correlation amongst bonafide and attack samples. These similarities in
intricate textural and morphological patterns of iris ocular images contribute
further to performance degradation. To alleviate these shortcomings, this paper
proposes DFCANet: Dense Feature Calibration and Attention Guided Network which
calibrates the locally spread iris patterns with the globally located ones.
Uplifting advantages from feature calibration convolution and residual
learning, DFCANet generates domain-specific iris feature representations. Since
some channels in the calibrated feature maps contain more prominent
information, we capitalize discriminative feature learning across the channels
through the channel attention mechanism. In order to intensify the challenge
for our proposed model, we make DFCANet operate over nonsegmented and
non-normalized ocular iris images. Extensive experimentation conducted over
challenging cross-domain and intra-domain scenarios highlights consistent
outperforming results. Compared to state-of-the-art methods, DFCANet achieves
significant gains in performance for the benchmark IIITD CLI, IIIT CSD and
NDCLD13 databases respectively. Further, a novel incremental learning-based
methodology has been introduced so as to overcome disentangled iris-data
characteristics and data scarcity. This paper also pursues the challenging
scenario that considers soft-lens under the attack category with evaluation
performed under various cross-domain protocols. The code will be made publicly
available.
