GAN-Based Image Augmentation for Finger-Vein Biometric Recognition

• Published in: IEEE access Vol. 7; pp. 183118 - 183132
• Main Authors: Zhang, Jianfeng, Lu, Zhiying, Li, Min, Wu, Haopeng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Augmentation; Biometrics; Biometrics (access control); convolutional neural networks; Datasets; Deep learning; Feature extraction; finger-vein classification; Gallium nitride; Generative adversarial networks; Image classification; Image recognition; Machine learning; Machine vision; Object recognition; Sample augmentation; Teaching methods; Training; Veins
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2960411

Deep learning methods, and especially convolutional neural networks (CNNs), have made a considerable breakthrough in various fields of machine vision, basically by employing large-scale labeled databases. However, deep learning methods applied in finger-vein area are basically implemented on small-scale datasets, which are probably faced with challenges such as overfitting, susceptible to finger position, unstable performance on various datasets and son on. In this study, we present a lightweight and fully convolutional Generative Adversarial Network (GAN) architecture, which is named FCGAN, using preliminary batch normalization, and tightly-constrained loss function for implementing finger-vein image augmentation. In addition, we present a novel scheme FCGAN-CNN for finger-vein classification, which reveals that synthetic finger-vein images using FCGAN are capable of improving the property of CNN for finger-vein image classification. The experiment of sample augmentation shows that the training accuracy using FCGAN-augmented samples could go beyond 99%, which is higher than 96.34% obtained using only classic sample augmentation. Furthermore, the well-trained CNN is further evaluated on a totally different dataset, which indicates that the proposed scheme FCGAN-CNN is capable of improving the ability of CNN to extract deep features. We consider that the proposed method for sample augmentation could be extended to other biometric systems.