Industrial Internet of Things (IIoT) realizes machine-to-machine communication and human–computer interaction (HCI) through communication network, which makes industrial production automatic and intelligent. Security is critical in IIoT because of the interconnection of intelligent industrial equipment. In IIoT environment, legitimate human–computer interaction can only be performed by authorized professionals, and unauthorized access is not tolerated. In this paper, a reliable authentication method based on biological information is proposed. Specifically, the complete local binary pattern (CLPB) and the statistical local binary pattern (SLPB) are introduced to describe the local vein texture characteristics. Meanwhile, the contrast energy and frequency domain information are regarded as auxiliary information to interpret the finger vein. The distance between the features of the registration image and the test image is used to recognize the finger vein image, so as to realize identity authentication. The experiments are carried out on SDUMLA-FV database and FV-USM database, and results show that the presented method has achieved high recognition accuracy.

工业物联网（IIoT）通过通信网络实现机器与机器的通信和人机交互（HCI），使工业生产自动化、智能化。由于智能工业设备的互连，安全性在 IIoT 中至关重要。在工业物联网环境中，合法的人机交互只能由授权的专业人员进行，不允许未经授权的访问。本文提出了一种基于生物信息的可靠认证方法。具体来说，引入完全局部二值模式（CLPB）和统计局部二值模式（SLPB）来描述局部静脉纹理特征。同时，对比能量和频域信息作为辅助信息来解释手指静脉。利用注册图像与测试图像的特征之间的距离来识别指静脉图像，从而实现身份认证。在SDUMLA-FV数据库和FV-USM数据库上进行了实验，结果表明所提出的方法具有较高的识别准确率。