  Iris recognition is a reliable personal identification method but there is
still much room to improve its accuracy especially in less-constrained
situations. For example, free movement of head pose may cause large rotation
difference between iris images. And illumination variations may cause irregular
distortion of iris texture. To match intra-class iris images with head rotation
robustly, the existing solutions usually need a precise alignment operation by
exhaustive search within a determined range in iris image preprosessing or
brute force searching the minimum Hamming distance in iris feature matching. In
the wild, iris rotation is of much greater uncertainty than that in constrained
situations and exhaustive search within a determined range is impracticable.
This paper presents a unified feature-level solution to both alignment free and
distortion robust iris recognition in the wild. A new deep learning based
method named Alignment Free Iris Network (AFINet) is proposed, which uses a
trainable VLAD (Vector of Locally Aggregated Descriptors) encoder called
NetVLAD to decouple the correlations between local representations and their
spatial positions. And deformable convolution is used to overcome iris texture
distortion by dense adaptive sampling. The results of extensive experiments on
three public iris image databases and the simulated degradation databases show
that AFINet significantly outperforms state-of-art iris recognition methods.
