In this letter, we propose a vein-based biometric verification system relying on deep learning. A novel approach consisting of a convolutional neural network (CNN), trained in a supervised manner, cascaded with an auto-encoder, trained in an unsupervised way, is here exploited. In more detail, a novel densely-connected convolutional autoencoder is here used on top of backbone CNNs. This architecture aims at increasing the discriminative capability of the features generated from hand vein patterns. Experimental tests on finger, palm, and dorsal veins show that the proposed approach leads to an improvement of the recognition rates with respect to the use of the sole CNNs for feature extraction. The achieved performance are superior to the current state of the art in vein biometric verification.

中文翻译：

---

使用密集连接的卷积自动编码器进行基于静脉的生物特征验证

在这封信中，我们提出了一种基于深度学习的基于静脉的生物特征验证系统。这里利用了一种新方法，该方法包括以监督方式训练的卷积神经网络 (CNN)，与以无监督方式训练的自动编码器级联。更详细地说，这里在主干 CNN 之上使用了一种新颖的密集连接的卷积自编码器。该架构旨在提高手部静脉图案生成的特征的判别能力。手指、手掌和背静脉的实验测试表明，相对于使用唯一的 CNN 进行特征提取，所提出的方法提高了识别率。所实现的性能优于静脉生物特征验证的当前技术水平。