Using CNNs to Identify the Origin of Finger Vein Image
This work addresses sensor identification for biometric security, but it is incremental as it applies existing CNN architectures to a new domain with minor architectural tweaks.
The paper tackled the problem of identifying the source sensor of finger vein images using deep learning, achieving near-perfect AUC-ROC scores of 1.0 for uncropped samples and 0.9997 for ROI samples.
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.