Triplet-Classifier GAN for Finger-Vein Verification

In finger-vein-based biometric verification, it is essential to robustly extract vein features with strong discrimination ability. Recently, deep learning methods have achieved remarkable performance in the field of finger-vein verification. However, the establishment of an effective deep learning model requires large-scale databases to prevent overfitting during the training process, while currently used finger-vein databases are not large enough. In our article, a new generative adversarial network (GAN), named triplet-classifier GAN, is designed for finger-vein verification. Unlike the traditional GAN-based method, the proposed triplet-classifier GAN uses the generated fake data to improve the learning ability of the triplet loss-based convolutional neural network (CNN) classifier. The combination of GAN and the triplet loss-based CNN classifier expands the training data and improves the discriminant ability of CNN. Experiments prove that the proposed triplet-classifier GAN has superior performance in finger-vein verification and has good prospects in finger-vein-based biometric verification.

Automated summary

In this article, a new generative adversarial network (GAN) called the triplet-classifier GAN is designed for finger-vein verification. Unlike traditional GAN methods, this model uses generated fake data to improve the learning ability of the classifier. Experiments prove that this model has superior performance in finger-vein verification and shows promise in finger-vein-based biometric verification.