目的 虹膜作为一种具有高稳定性与区分性的生物特征，使得虹膜识别在应用场景中十分普及，但很多虹膜识别系统在抵御各类演示攻击时无法保证十足的可靠性，导致虹膜识别在高级安全场景中的应用受限，使得虹膜活体检测成为生物识别技术中亟需解决的问题之一。现有的区分真实与假体虹膜最先进的算法主要依靠在原始灰度空间中提取的虹膜纹理深度特征，但这类特征差异不明显，只能辨别单源假体虹膜。为此，提出一种基于增强型灰度图像空间的虹膜活体检测方法。方法 利用残差网络（ResNet）将原始虹膜图像映射到可分离的灰度图像空间，使真假虹膜特征具有明显的判别性；用预训练LightCNN （light convolational neural networks）-4网络提取新空间中的虹膜纹理特征；设计三元组损失函数与softmax损失函数训练模型实现二分类任务。结果 在两个单源假虹膜数据库上采用闭集检测方式分别取得100%和99.75%的准确率；在多源假虹膜数据库上采用开集检测方式分别取得98.94%和99.06%的准确率。结论 本文方法通过空间映射的方式增强真假虹膜纹理之间清晰度的差异，设计三元组损失函数与softmax损失函数训练模型，既增加正负样本集之间的距离差，又提升模型收敛速度。实验结果表明，基于图像空间的分析与变换可有效解决真实虹膜与各类假体虹膜在原始灰度空间中不易区分的问题，并且使网络能够准确检测未知类型的假体虹膜样本，实现虹膜活体检测的最新性能，进一步提升了虹膜活体检测方法的泛化性。

Objective Iris refers to the annular area between the black pupil and the white sclera of human eyes. It is a biological characteristic with high stability and discrimination ability. Therefore, the iris recognition technology has already been applied to various practical scenarios. However, many iris recognition systems still fail to assure full reliability when facing different presentation attacks. Thus, iris recognition systems cannot be deployed in conditions with strict security requirements. Iris anti-spoofing is a concerned problem in biometrics demanding robust solutions. Iris anti-spoofing (or iris presentation attack detection) aims to judge whether the input iris image is captured from a living subject. This technology is important to prevent invalid authentication from spoofing attacks and protect the security or benefits of users; it needs to be applied to all authentication systems. Existing state-of-the-art algorithms mainly rely on the deep features of iris textures extracted in original gray-scaled space to distinguish fake irises from genuine ones. However, these features can identify only single fake iris pattern, and genuine and various fake irises are overlapped in the original gray-scaled space. A novel iris anti-spoofing method is thus proposed in this study. The triplet network is equipped with a space transformer that is designed to map the original gray-scaled space into a newly enhanced gray-level space, in which the iris features between a specific genuine iris and various fake irises are highly discriminative. Method A raw eye image includes sclera, pupil, iris, and periocular areas, but only the information of iris is needed. Consequently, the raw image should be preprocessed before image space mapping. The preprocessing mainly includes iris detection, localization, segmentation, and normalization. After a series of image-preprocessing steps, the original gray-scaled normalized iris images are mapped into a newly enhanced gray-level space using the residual network, in which the iris features are highly discriminative. The pretrained LightCNN (light convolutional neural networks)-4 network is used to extract deep features of iris images in the new space. Triplet and softmax losses are adopted to train the model to accomplish a binary classification task. Result We evaluate the proposed method on three available iris image databases. The ND-Contact (notre dame cosmetic contact lenses) and CRIPAC (center for research on intelligent perception and computing)-Printed-Iris databases include only cosmetic contact lenses and printed iris images, respectively. The CASIA (Institute of Automation, Chinese Academy of Sciences)-Iris-Fake database includes various fake iris patterns, such as cosmetic contact lens, printed iris, plastic iris (fake iris textures are printed on plastic eyeball models), and synthetic iris (image generation technology based on GAN(generative adversarial networks)). CRIPAC-Printed-Iris is a newly established fake iris database by us for researching on anti-spoofing attack detection algorithms. We perform evaluations in two different detection settings, namely, “close set” and “open set” detection. The “close set” setup means that the training and testing sets share the same type of presentation attack. The “open set” setup refers to the condition in which the presentation attacks unseen in the training set may appear in the testing set; it is considered in our experiments to provide a comprehensive analysis of the proposed method. Experimental results show that the proposed method can achieve accuracy of 100% and 99.75% on two single fake iris pattern databases in the “close set” detection setup. For the hybrid fake iris pattern database, the proposed method can achieve accuracies of 98.94% and 99.06% in the “open set” detection setup. The ablation study shows that the deep features of iris images in the enhanced gray-level image space are more discriminative than those in the original gray-scaled space. Conclusion The proposed method first enhances the difference in sharpness between genuine and fake iris textures by a space-mapping approach. Then, it minimizes the intraclass distance, maximizes the interclass distance, and keeps a safe margin by triplet loss between genuine and fake iris samples. Thus, the classification accuracy is improved. The model convergence speed is improved by softmax loss. The triplet loss only aims to local samples; as a result, the network training is unstable, and the convergence speed is slow. Thus, we combine the softmax loss to train the classification network, which can provide a global classification interface (aiming to global samples) to increase the convergence speed of network training. Experimental results demonstrate that the analysis and transformation based on image space can effectively solve the difficulty in separating genuine iris and various types of fake irises in the original gray-scaled space. To the best of our knowledge, this is the first time a deep network can distinguish a genuine iris from various types of fake irises because an obvious difference exists between the genuine iris image and various fake iris images in the enhanced gray-level image space. In the “close set” and “open set” detection settings, the trained network can accurately identify the deep features of a specific genuine iris image and distinguish it from various other fake iris images. Therefore, our proposed method can achieve a state-of-the-art performance in iris anti-spoofing, which indicates its effectiveness. The proposed method further enriches and perfects the generalization of a detection method for iris anti-spoofing attacks.