GAN-Based Image Augmentation for Finger-Vein Biometric Recognition

Abstract

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. INDEX TERMS Sample augmentation, convolutional neural networks, generative adversarial networks, finger-vein classification.

Towards the Generation of Synthetic Images of Palm Vein Patterns: A Review

“…In summary, palm vein images are affected mainly because the acquisition devices are faced with uncontrolled parameters such as uneven illumination and hand position. Additionally, they are affected by device-independent parameters related to soft biometrics and lack of robustness due to outdoor illumination [62,19]. To deal with the above, different acquisition devices have been proposed.…”

“…However, the proposed methods are based on two generalist techniques that do not explicitly consider the topology of the palm vascular system. Moreover, other studies have provided methodologies for the generation of synthetic vein images such as dorsal hand veins [13], finger-vein images [4,19,20], and sclera vascular network [21]. However, it could be argued that the simulation of palm vein patterns is a difficult task due to the complex vein structures of the hand [22,23].…”

A Novel Finger Vein Recognition Method Based on Aggregation of Radon-Like Features

“…Recently, some more powerful but complex network models, such as Siamese Network [ 46 ], GaborPCA Network [ 47 , 48 ], Convolutional Autoencoder [ 49 ], Capsule Network [ 50 ], DenseNet [ 51 , 52 ], Fully Convolutional Network (FCN) [ 53 , 54 ], Generative Adversarial Network (GAN) [ 55 , 56 , 57 ], and Long Short-term Memory (LSTM) Network [ 58 ], etc., also emerged in the field of FV image recognition. Especially for the GAN, which can not only achieve more robust vein patterns from low-quality FV images, but also generate a variety of synthetic FV samples.…”

Convolutional Neural Network (CNN)-Based Fast Back Projection Imaging With Noise-Resistant Capability

“…In recent years, deep learning technology, especially the convolutional neural network (CNN) has emerged as a powerful tool for image construction and processing [16][17][18]. Previously, the CNN has been successfully applied to implement speckle elimination [19,20], target classification [21,22], and recognition [23] in the field of SAR imaging.…”