Second-order convolutional networks for iris recognition

doi:10.1007/s10489-021-02925-y

2022-01-22

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DOI：10.1007/s10489-021-02925-y

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Iris recognition in less constrained environments is challenging as the images taken therein contain severe noisy factors. How to represent iris texture for accurate and robust recognition in such environments is still an open issue. Towards addressing this problem, this paper proposes a novel convolutional network (ConvNet) for effective iris texture representation. The key of the proposed ConvNet is an interaction block which computes an affinity matrix among all pairwise high-level features for learning second-order relationships. The interaction block can model relationships of neighboring and long-range features, and is architecture-agnostic, suitable for different deep network architectures. To further improve the robustness of iris representation, we encode the affinity matrix based on ordinal measure. In addition, we develop a mask network corresponding to the feature learning network, which can exclude the noisy factors during iris matching. We perform thorough ablation studies to evaluate the effectiveness of the proposed networks. Experiments have shown that the proposed networks outperform state-of-the-art (SOTA) methods, achieving a false reject rate (FRR) of 5.49%, 10.41% and 5.80% at 10(- 6) false accept rate (FAR) on ND-IRIS-0405, CASIA-IrisV4-Thousand and CASIA-IrisV4-Lamp respectively. And the improvements in equal error rates (EERs) are 0.41%, 0.72% and 0.40%, respectively, as compared with the SOTA methods.

Keyword:

Iris recognition

Convolutional networks

Second-order relationships

Affinity matrix