Recognition of Image-Orientation-Based Iris Spoofing
Recognition of Image-Orientation-Based Iris Spoofing
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DOI：
10.1109/tifs.2017.2701332
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发表时间：
2017-09-01
影响因子：
6.8
通讯作者：
VidalMata, Rosaura G.
中科院分区：
文献类型：
--
作者：
Czajka, Adam;Bowyer, Kevin W.;VidalMata, Rosaura G.
本文提出了一种自动识别虹膜图像的正确左/右和垂直/倒置方向的解决方案。该解决方案可用于通过在采集过程中旋转虹膜图像或虹膜传感器来对抗旨在生成虚假身份的欺骗攻击。在相同的数据上比较两种方法，使用相同的评估协议：1)特征工程，使用支持向量机（SVM）手工制作的特征分类；2)特征学习，使用卷积神经网络（CNN）学习和分类的数据驱动特征。使用4个传感器获取103名受试者的20 750张虹膜图像数据集用于开发。另外还有来自32个额外受试者的1,939张图像，用于测试目的。进行了相同传感器和交叉传感器测试，以研究分类方法如何推广到未知硬件。基于svm的方法在主体分离数据和相机分离数据上的左/右（直立/倒置）方向识别的平均正确分类率超过95%(89%)，如果图像是由同一传感器获取的，则平均正确分类率超过99%（97%）。与支持向量机相比，基于cnn的方法在相同传感器实验中表现更好，对未知传感器的泛化能力略差。我们没有看到其他关于自动识别虹膜图像的垂直/倒置方向，或者在同一传感器和跨传感器主体分离实验中研究手工制作和数据驱动的特征的论文。本文中使用的数据集，以及交叉验证中使用的数据的随机分割，都是可用的。
This paper presents a solution to automatically recognize the correct left/right and upright/upside-down orientation of iris images. This solution can be used to counter spoofing attacks directed to generate fake identities by rotating an iris image or the iris sensor during the acquisition. Two approaches are compared on the same data, using the same evaluation protocol: 1) feature engineering, using hand-crafted features classified by a support vector machine (SVM) and 2) feature learning, using data-driven features learned and classified by a convolutional neural network (CNN). A data set of 20 750 iris images, acquired for 103 subjects using four sensors, was used for development. An additional subject-disjoint data set of 1,939 images, from 32 additional subjects, was used for testing purposes. Both same-sensor and cross-sensor tests were carried out to investigate how the classification approaches generalize to unknown hardware. The SVM-based approach achieved an average correct classification rate above 95% (89%) for recognition of left/right (upright/upside-down) orientation when tested on subject-disjoint data and camera-disjoint data, and 99% (97%) if the images were acquired by the same sensor. The CNN-based approach performed better for same-sensor experiments, and presented slightly worse generalization capabilities to unknown sensors when compared with the SVM. We are not aware of any other papers on the automatic recognition of upright/upside-down orientation of iris images, or studying both hand-crafted and data-driven features in same-sensor and cross-sensor subject-disjoint experiments. The data sets used in this paper, along with random splits of the data used in cross-validation, are being made available.