Alignment Free and Distortion Robust Iris Recognition

TL;DR

This paper introduces AFINet, a deep learning approach that achieves alignment-free and distortion-robust iris recognition in unconstrained environments by using NetVLAD encoding and deformable convolutions.

Contribution

The paper proposes a novel deep learning framework combining NetVLAD and deformable convolutions for robust, alignment-free iris recognition in challenging, real-world conditions.

Findings

AFINet outperforms existing iris recognition methods on public datasets.

The method effectively handles head pose variations and texture distortions.

Extensive experiments validate the robustness and accuracy of the approach.

Abstract

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.