Using CNNs to Identify the Origin of Finger Vein Image

TL;DR

This paper demonstrates that CNN architectures, including a novel compact model, significantly improve finger vein sensor identification accuracy over traditional methods, achieving near-perfect AUC-ROC scores.

Contribution

Introduction of a new compact CNN architecture, FV2021, for finger vein sensor identification, outperforming existing correlation and texture-based methods.

Findings

Achieved AUC-ROC scores of 1.0 and 0.9997 on different datasets.

CNN models outperform traditional correlation-based methods.

The novel FV2021 architecture is efficient and highly accurate.

Abstract

We study the finger vein (FV) sensor model identification task using a deep learning approach. So far, for this biometric modality, only correlation-based PRNU and texture descriptor-based methods have been applied. We employ five prominent CNN architectures covering a wide range of CNN family models, including VGG16, ResNet, and the Xception model. In addition, a novel architecture termed FV2021 is proposed in this work, which excels by its compactness and a low number of parameters to be trained. Original samples, as well as the region of interest data from eight publicly accessible FV datasets, are used in experimentation. An excellent sensor identification AUC-ROC score of 1.0 for patches of uncropped samples and 0.9997 for ROI samples have been achieved. The comparison with former methods shows that the CNN-based approach is superior and improved the results.