虹膜防欺骗的统一框架：引入虹膜防欺骗跨域测试协议与 Masked-MoE 方法
A Unified Framework for Iris Anti-Spoofing: Introducing Iris Anti-Spoofing Cross-Domain-Testing Protocol and Masked-MoE Method
AI总结：
本文针对虹膜防欺骗跨域评估缺失问题，提出包含10个数据集、覆盖跨种族与跨设备测试的IAS-CDT协议，并设计Masked-MoE方法通过随机屏蔽token缓解MoE过拟合，提升跨设备泛化能力。
摘要：
虹膜识别因其稳定性和独特性而被广泛应用于高安全性场景。然而，不同设备采集的虹膜图像表现出与设备相关的特定一致差异，这对防欺骗分类算法产生了较大影响。不同种族的虹膜也会影响分类，从而带来身份盗用的风险。因此，有必要提升虹膜防欺骗（IAS）方法的跨域能力，使其在面对不同种族和设备时更加鲁棒。然而，目前尚不存在全面可用的协议。为填补这一空白，我们提出了虹膜防欺骗跨域测试（IAS-CDT）协议，该协议涉及 10 个数据集，分属 7 个数据库，由 4 家机构发布，并使用 6 种不同设备采集。该协议包含三个层次化的子协议，分别用于评估 IAS 模型的平均性能、跨种族泛化能力和跨设备泛化能力。此外，为应对 IAS-CDT 协议带来的跨设备泛化挑战，我们采用多组模型参数从多个子数据集中进行学习。具体而言，我们利用专家混合（Mixture of Experts，MoE）通过多个子神经网络拟合复杂数据分布。为进一步增强泛化能力，我们提出了一种新方法 Masked-MoE（MMoE），该方法随机为部分专家屏蔽一部分 token，并要求其输出与未屏蔽专家相似，从而有效缓解 MoE 的过拟合问题。在评估方面，我们选取 ResNet50、VIT-B/16、CLIP 和 FLIP 作为代表性模型，并在所提出的 IAS-CDT 协议下对它们进行了基准测试。
英文摘要：
Iris recognition is widely used in high-security scenarios due to its stability and distinctiveness. However, iris images captured by different devices exhibit certain and device-related consistent differences, which has a greater impact on the classification algorithm for anti-spoofing. The iris of various races would also affect the classification, causing the risk of identity theft. So it is necessary to improve the cross-domain capabilities of the iris anti-spoofing (IAS) methods to enable it more robust in facing different races and devices. However, there is no existing protocol that is comprehensively available. To address this gap, we propose an Iris Anti-Spoofing Cross-Domain-Testing (IAS-CDT) Protocol, which involves 10 datasets, belonging to 7 databases, published by 4 institutions, and collected with 6 different devices. It contains three sub-protocols hierarchically, aimed at evaluating average performance, cross-racial generalization, and cross-device generalization of IAS models. Moreover, to address the cross-device generalization challenge brought by the IAS-CDT Protocol, we employ multiple model parameter sets to learn from the multiple sub-datasets. Specifically, we utilize the Mixture of Experts (MoE) to fit complex data distributions using multiple sub-neural networks. To further enhance the generalization capabilities, we propose a novel method Masked-MoE (MMoE), which randomly masks a portion of tokens for some experts and requires their outputs to be similar to the unmasked experts, which can effectively mitigate the overfitting issue of MoE. For the evaluation, we selected ResNet50, VIT-B/16, CLIP, and FLIP as representative models and benchmarked them under the proposed IAS-CDT Protocol.
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