Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea.
Sensors (Basel). 2018 Aug 8;18(8):2601. doi: 10.3390/s18082601.
Iris recognition systems have been used in high-security-level applications because of their high recognition rate and the distinctiveness of iris patterns. However, as reported by recent studies, an iris recognition system can be fooled by the use of artificial iris patterns and lead to a reduction in its security level. The accuracies of previous presentation attack detection research are limited because they used only features extracted from global iris region image. To overcome this problem, we propose a new presentation attack detection method for iris recognition by combining features extracted from both local and global iris regions, using convolutional neural networks and support vector machines based on a near-infrared (NIR) light camera sensor. The detection results using each kind of image features are fused, based on two fusion methods of feature level and score level to enhance the detection ability of each kind of image features. Through extensive experiments using two popular public datasets (LivDet-Iris-2017 Warsaw and Notre Dame Contact Lens Detection 2015) and their fusion, we validate the efficiency of our proposed method by providing smaller detection errors than those produced by previous studies.
虹膜识别系统由于其高识别率和虹膜模式的独特性，已被用于高安全级别的应用中。然而，正如最近的研究报告所指出的，虹膜识别系统可能会被人为的虹膜模式所欺骗，从而降低其安全级别。以前的呈现攻击检测研究的准确性受到限制，因为它们只使用从全局虹膜区域图像中提取的特征。为了克服这个问题，我们提出了一种新的虹膜识别呈现攻击检测方法，通过使用卷积神经网络和基于近红外（NIR）光相机传感器的支持向量机，结合从局部和全局虹膜区域提取的特征。基于特征级和评分级的两种融合方法对每种图像特征的检测结果进行融合，以增强每种图像特征的检测能力。通过使用两个流行的公共数据集（华沙 LivDet-Iris-2017 和 2015 年圣母大学隐形眼镜检测）及其融合进行广泛的实验，我们通过提供比以前的研究更小的检测误差来验证我们提出的方法的有效性。
