基于计算光场成像的虹膜活体检测方法

光场成像相对传统光学成像是一次重大技术革新，高维光场信息为生物特征识别的发展与创新带来了新机遇.虹膜身份识别技术以其唯一性、稳定性、高精度等优势广泛应用于国防、教育、金融等各个领域，但是现有的虹膜识别系统容易被人造假体虹膜样本欺骗导致误识别.因此，虹膜活体检测是当前虹膜识别研究亟待解决的关键问题.本文提出一种基于计算光场成像的虹膜活体检测方法，通过软硬件结合的方式，充分挖掘四维光场数据的信息.本方法使用实验室自主研发的光场相机采集光场虹膜图像，利用光场数字重对焦技术提取眼周区域的立体结构特征和虹膜图像的纹理特征，进行特征融合与虹膜分类.在自主采集的近红外光场虹膜活体检测数据库上进行实验，本方法的平均分类错误率（Average classification error rate，ACER）为3.69%，在现有最佳方法的基础上降低5.94%.实验结果表明本方法可以准确有效地检测并阻止打印虹膜和屏显虹膜对系统的攻击.

Iris Liveness Detection Based on Light Field Imaging

Light-field (LF) imaging is a new method to capture both intensity and direction information of visual objects, providing promising solutions to biometrics. Iris recognition is a reliable personal identification method, however it is also vulnerable to spoofing attacks, such as iris patterns printed on contact lens or paper. Therefore iris liveness detection is an important module in iris recognition systems. In this paper, an iris liveness detection approach is proposed to take full advantages of intrinsic characteristics in light-field iris imaging. LF iris images are captured by using lab-made LF cameras, based on which the geometric features as well as the texture features are extracted using the LF digital refocusing technology. These features are combined for genuine and fake iris image classification. Experiments were carried out based on the self-collected near-infrared LF iris database, and the average classification error rate (ACER) of the proposed method is 3.69%, which is 5.94% lower than the best state-of-the-art method. Experimental results indicate the proposed method is able to work effectively and accurately to prevent spoofing attacks such as printed and screen-displayed iris input attacks.