Toward More Accurate Heterogeneous Iris Recognition with Transformers and Capsules.

As diverse iris capture devices have been deployed, the performance of iris recognition under heterogeneous conditions, e.g., cross-spectral matching and cross-sensor matching, has drastically degraded. Nevertheless, the performance of existing manual descriptor-based methods and CNN-based methods is limited due to the enormous domain gap under heterogeneous acquisition. To tackle this problem, we propose a model with transformers and capsules to extract and match the domain-invariant feature effectively and efficiently. First, we represent the features from shallow convolution as vision tokens by spatial attention. Then we model the high-level semantic information and fine-grained discriminative features in the token-based space by a transformer encoder. Next, a Siamese transformer decoder exploits the relationship between pixel-space and token-based space to enhance the domain-invariant and discriminative properties of convolution features. Finally, a 3D capsule network not only efficiently considers part-whole relationships but also increases intra-class compactness and inter-class separability. Therefore, it improves the robustness of heterogeneous recognition. Experimental validation results on two common datasets show that our method significantly outperforms the state-of-the-art methods.