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FV-GAN: Finger Vein Representation Using Generative Adversarial Networks

Yang, W; Hui, C; Chen, Z; Xue, J-H; Liao, Q; (2019) FV-GAN: Finger Vein Representation Using Generative Adversarial Networks. IEEE Transactions on Information Forensics and Security , 14 (9) pp. 2512-2524. 10.1109/TIFS.2019.2902819. Green open access

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Abstract

In finger vein verification, the most important and challenging part is to robustly extract finger vein patterns from low-contrast infrared finger images with limited a priori knowledge. Although recent convolutional neural network (CNN)-based methods for finger vein verification have shown powerful capacity for feature representation and promising perspective in this area, they still have two critical issues to address. First, these CNN-based methods unexceptionally utilize fully connected layers, which restrict the size of finger vein images to process and increase the processing time. Second, the capacity of CNN for feature representation generally suffers from the low quality of finger vein ground-truth pattern maps for training, particularly due to outliers and vessel breaks. To address these issues, in this paper, we propose a novel approach termed FV-GAN to finger vein extraction and verification, based on generative adversarial network (GAN), as the first attempt in this area. Unlike the CNN-based methods, FV-GAN learns from the joint distribution of finger vein images and pattern maps rather than the direct mapping between them, with the aim at achieving stronger robustness against outliers and vessel breaks. Moreover, FV-GAN adopts fully convolutional networks as the basic architecture and discards fully connected layers, which relaxes the constraint on the input image size and reduces the computational expenditure for feature extraction. Furthermore, we design an adversarial training strategy and propose a hybrid loss function for FV-GAN. The experimental results on two public databases show significant improvement by FV-GAN in finger vein verification in terms of both verification accuracy and equal error rate.

Type: Article
Title: FV-GAN: Finger Vein Representation Using Generative Adversarial Networks
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TIFS.2019.2902819
Publisher version: https://doi.org/10.1109/TIFS.2019.2902819
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Finger vein verification, pattern extraction, convolutional neural network (CNN), generative adversarial network (GAN), cycle-consistent adversarial network (CycleGAN), deep convolutional generative adversarial network (DCGAN), U-Net
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10078214
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