Iris Presentation Attack Detection (PAD) is critical for securing recognition systems, yet its practical deployment is severely hindered by the poor generalization of models across different acquisition devices and diverse datasets. To address this persistent cross-domain challenge, we first introduce a comprehensive evaluation framework, the Iris Presentation Attack Detection Cross-Domain-Testing (IPAD-CDT) Protocol, designed to evaluate the model robustness in these scenarios. Our core contribution is a novel Masked Mixture-of-Experts (MMoE) method, which enhances the generalization of Transformer-based architectures. MMoE introduces a structured information asymmetry, where "student" Experts learn robust features from masked inputs by distilling knowledge from an unmasked "teacher" Expert via a cosine distance loss. This mask-and-distill mechanism effectively mitigates overfitting and guides the model to learn domain-invariant cues. By integrating MMoE into a CLIP-based model, we conduct extensive experiments on our IPAD-CDT protocol. The results demonstrate that our method sets a new state-of-the-art, significantly outperforming existing models, especially in the challenging cross-dataset and cross-device settings.

The cosmetic contact lenses over an iris may change original iris textural pattern which is the foundation for iris recognition, making the cosmetic lenses a possible and easy-to-use iris presentation attack means. For practical application scenes, the cosmetic contact lenses detection still facing unsolved problems, due to the low image quality and difficulty in accurately iris localization. In this paper, we propose a novel framework called Weighted Region Network (WRN) to detect the cosmetic contact lenses. The WRN includes a local attention Weight Network and a global classification Region Network. With the inherent attention mechanism, the proposed network is able to find more discriminative regions, which reduces the requirement for target detection and improves the ability of classification. The Weight Network can be trained by using Rank loss and MSE loss without manual discriminative region annotations. Experiments are conducted on several public databases and a new collected low-quality iris image database. The proposed method outperforms state-of-the-art fake iris detection algorithms, and is also effective for the fine-grained image classification task.

With the development of iris recognition, many identity authentication applications began to use this inherent biometric ID. Despite the breakthroughs in the identification with iris recognition technology, one primary problem remains unsolved: the presentation spoof attack. In this paper, we present a novel algorithm 4DCycle-GAN for expanding the spoof iris image database by synthesizing fake iris images wearing textured contact lenses. The proposed 4DCycle-GAN follows the Cycle-Consistent Adversarial Networks (Cycle-GAN) framework which translating between one kind images (genuine iris images) and one other kind images (textured contact lenses iris images). The 4DCycle-GAN introduces two more discriminators to improve the Cycle-GAN at the defect of lack of diversity. The two new discriminators `prefer' images generated by the generators, while the original discriminators in Cycle-GAN `prefer' real captured images. These new added confrontations make the 4DCycle-GAN avoid generating a certain kind of contact lenses texture which is larger percentage of the training iris database. The synthesized textured contact lenses iris images are used for spoofing iris detection training to improve the robustness of classification algorithm. Both the Cycle-GAN and the 4DCycle-GAN synthesizing images can improve the spoof classification results. Moreover, by using the 4DCycle-GAN, the spoof classification results are distinctly improved for unrelated non-homologous database experiments. Extensive experimental results show that the proposed method can improve the anti-spoof ability of iris recognition system.

Matching heterogeneous iris images in less constrained applications of iris biometrics is becoming a challenging task. The existing solutions try to reduce the difference between heterogeneous iris images in pixel intensities or filtered features. In contrast, this paper proposes a code-level approach in heterogeneous iris recognition. The non-linear relationship between binary feature codes of heterogeneous iris images is modeled by an adapted Markov network. This model transforms the number of iris templates in the probe into a homogenous iris template corresponding to the gallery sample. In addition, a weight map on the reliability of binary codes in the iris template can be derived from the model. The learnt iris template and weight map are jointly used in building a robust iris matcher against the variations of imaging sensors, capturing distance, and subject conditions. Extensive experimental results of matching cross-sensor, high-resolution versus low-resolution and, clear versus blurred iris images demonstrate the code-level approach can achieve the highest accuracy in compared with the existing pixel-level, feature-level, and score-level solutions.

Iris recognition becomes an important technology in our society. Visual patterns of human iris provide rich texture information for personal identification. However, it is greatly challenging to match intra-class iris images with large variations in unconstrained environments because of noises, illumination variation, heterogeneity and so on. To track current state-of-the-art algorithms in iris recognition, we organized the first ICB* Competition on Iris Recognition in 2013 (or ICIR2013 shortly). In this competition, 8 participants from 6 countries submitted 13 algorithms totally. All the algorithms were trained on a public database (e.g. CASIA-Iris-Thousand [3]) and evaluated on an unpublished database. The testing results in terms of False Non-match Rate (FNMR) when False Match Rate (FMR) is 0.0001 are taken to rank the submitted algorithms.