Abstract: Finger vein recognition technology has become a research hotspot in the new generation of biometrics because of its advantages of non-contact, high security and living body detection. With the development of deep learning, finger vein recognition technology based on deep neural network has made remarkable achievements. This paper firstly introduces the common public datasets in the field of finger vein recognition, and then classifies the applications of deep learning methods in finger vein recognition in recent years according to different neural network learning tasks, and analyzes the technical characteristics and application scenarios of each type. This paper also introduces the design techniques of deep learning in finger vein recognition from the aspects of lightweight network, data augmentation, attention mechanism and so on, and then expounds the common loss function in the model from two aspects of classifying loss and measuring learning loss. Finally, the evaluation indices of finger vein recognition system are introduced and the results of some researches on accuracy and equal error rate are summarized. In addition, the challenges and potential development directions of finger vein recognition are also presented.

Key words: finger vein recognition, deep learning, deep neural network, convolutional neural networks (CNN)

摘要： 手指静脉识别技术由于其非接触、高防伪性以及活体检测等优点，成为新一代生物识别技术中的研究热点。随着深度学习的发展，基于深度神经网络的手指静脉识别技术取得了显著的成果。首先对手指静脉识别领域的常用公开数据集进行了介绍，然后根据神经网络学习任务的不同，对近几年深度学习方法在手指静脉识别中的应用进行了分类，分析了每种类型的技术特点和适用场景。从轻量化网络、数据增广、注意力机制等方面对手指静脉识别中的深度学习设计技巧进行了介绍。从分类损失和度量学习损失两方面，对模型中常用的损失函数进行了阐述。最后介绍了手指静脉识别系统的评价指标并汇总了部分研究在准确率和等错误率方面的成果。此外，还提出了手指静脉识别面临的挑战和潜在的发展方向。

关键词: 手指静脉识别, 深度学习, 深度神经网络, 卷积神经网络（CNN）