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Deep Learning Multi-layer Fusion for an Accurate Iris Presentation Attack Detection

机译：深度学习多层融合，用于准确的虹膜呈现攻击检测

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Iris presentation attack detection (PAD) algorithms are developed to address the vulnerability of iris recognition systems to presentation attacks. Taking into account that the deep features successfully improved computer vision performance in various fields including iris recognition, it is natural to use features extracted from deep neural networks for iris PAD. Each layer in a deep learning network carries features of different level of abstraction. The features extracted from the first layer to the higher layers become more complex and more abstract. This might point our complementary information in these features that can collaborate towards an accurate PAD decision. Therefore, we propose an iris PAD solution based on multi-layer fusion. The information extracted from the last several convolutional layers are fused on two levels, feature-level and score-level. We demonstrated experiments on both, off-the-shelf pre-trained network and network trained from scratch. An extensive experiment also explores the complementary between different layer combinations of deep features. Our experimental results show that feature-level based multi-layer fusion method performs better than the best single layer feature extractor in most cases. In addition, our fusion results achieve similar or better results than the state-of-the-art algorithms on the Notre Dame and IIITD-WVU databases of the Iris Liveness Detection Competition 2017 (LivDet-Iris 2017).
机译：虹膜演示攻击检测（PAD）算法的开发旨在解决虹膜识别系统对演示攻击的脆弱性。考虑到深度特征在包括虹膜识别在内的各个领域中成功提高了计算机视觉性能，因此很自然地使用从深度神经网络中提取的特征进行虹膜PAD。深度学习网络中的每一层都具有不同抽象级别的功能。从第一层提取到更高层的特征变得更加复杂和抽象。这可能会在这些功能中指出我们的补充信息，这些信息可以协作以做出准确的PAD决策。因此，我们提出了一种基于多层融合的虹膜PAD解决方案。从最后几个卷积层提取的信息被融合在两个级别上，即特征级别和得分级别。我们在现成的预训练网络和从头开始训练的网络上都演示了实验。广泛的实验还探索了深层特征的不同层组合之间的互补性。我们的实验结果表明，在大多数情况下，基于特征级别的多层融合方法的性能要优于最佳的单层特征提取器。此外，我们的融合结果比2017年虹膜活力检测竞赛（LivDet-Iris 2017）的Notre Dame和IIITD-WVU数据库上的最新算法具有相似或更好的结果。

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