论文标题
跨数据库设置下的广义虹膜呈现攻击检测算法
Generalized Iris Presentation Attack Detection Algorithm under Cross-Database Settings
论文作者
论文摘要
演示攻击对大多数生物识别方式构成了重大挑战。 IRIS识别被认为是人识别最准确的生物识别方式之一，也已被证明很容易受到高级演示攻击的影响，例如3D隐形眼镜和纹理镜头。在文献中，提出了几种演示攻击检测（PAD）算法。一个重要的限制是针对看不见的数据库，看不见的传感器和不同成像环境的普遍性。为了应对这一挑战，我们提出了一个普遍的基于深度学习的PAD网络Mvanet，该网络利用多个表示层。它的灵感来自混合算法或多个检测网络的融合的简单性和成功。计算复杂性是训练深神经网络的重要因素。因此，为了在学习多个特征表示层时降低计算复杂性，已经使用了固定基本模型。在跨数据库训练测试设置下的多个数据库中，在多个数据库（例如IIITD-WVU MUIPA和IIITD-CLI数据库）上进行了表明，以评估提出算法的普遍性。
Presentation attacks are posing major challenges to most of the biometric modalities. Iris recognition, which is considered as one of the most accurate biometric modality for person identification, has also been shown to be vulnerable to advanced presentation attacks such as 3D contact lenses and textured lens. While in the literature, several presentation attack detection (PAD) algorithms are presented; a significant limitation is the generalizability against an unseen database, unseen sensor, and different imaging environment. To address this challenge, we propose a generalized deep learning-based PAD network, MVANet, which utilizes multiple representation layers. It is inspired by the simplicity and success of hybrid algorithm or fusion of multiple detection networks. The computational complexity is an essential factor in training deep neural networks; therefore, to reduce the computational complexity while learning multiple feature representation layers, a fixed base model has been used. The performance of the proposed network is demonstrated on multiple databases such as IIITD-WVU MUIPA and IIITD-CLI databases under cross-database training-testing settings, to assess the generalizability of the proposed algorithm.
