Iris Presentation Attack Detection (PAD) is essential to secure iris recognition systems. Recent iris PAD solutions achieved good performance by leveraging deep learning techniques. However, most results were reported under intra-database scenarios and it is unclear if such solutions can generalize well across databases and capture spectra. These PAD methods run the risk of overfitting because of the binary label supervision during the network training, which serves global information learning but weakens the capture of local discriminative features. This chapter presents a novel attention-based deep pixel-wise binary supervision (A-PBS) method. A-PBS utilizes pixel-wise supervision to capture the fine-grained pixel/patch-level cues and attention mechanism to guide the network to automatically find regions where most contribute to an accurate PAD decision. Extensive experiments are performed on six NIR and one visible-light iris databases to show the effectiveness and robustness of proposed A-PBS methods. We additionally conduct extensive experiments under intra-/cross-database and intra-/cross-spectrum for detailed analysis. The results of our experiments indicates the generalizability of the A-PBS iris PAD approach.

翻译：摘要：虹膜呈现攻击检测对于保障虹膜识别系统的安全性至关重要。近年来，基于深度学习技术的虹膜呈现攻击解决方案取得了良好性能。然而，大多数结果是在数据库内场景下报告的，尚不清楚此类解决方案能否在跨数据库和不同捕获光谱下具有良好的泛化能力。这类呈现攻击检测方法因网络训练过程中依赖二元标签监督而存在过拟合风险——这种监督有助于全局信息学习，但削弱了对局部判别性特征的捕捉。本章提出了一种新颖的基于注意力机制的深度像素级二元监督方法。该方法通过像素级监督捕捉细粒度像素/图像块线索，并利用注意力机制引导网络自动定位对精确呈现攻击检测决策贡献最大的区域。我们在六个近红外数据库和一个可见光虹膜数据库上进行了大量实验，证明所提出的注意力机制深度像素级二元监督方法的有效性和鲁棒性。此外，我们还对数据库内/跨数据库以及光谱内/跨光谱场景开展了广泛实验以进行详细分析。实验结果表明，注意力机制深度像素级二元监督虹膜呈现攻击检测方法具有良好的泛化能力。