Finger-vein Recognition using Deep Fully Convolutional Neural Semantic Segmentation Networks: The Impact of Training Data

We propose a novel approach for finger-vein recognition, focused on direct extraction of actual finger-vein patterns from NIR finger images without any specific pre- or post-processing, using semantic segmentation convolutional neural networks (CNNs). We utilize three network architectures and besides identifying efficient training and configuration settings for these networks, using manually annotated training data, we present a training model based on automatically generated labels to improve the networks' performance. Based on our experimental results, the proposed model can achieve superior performance over traditional finger-vein recognition algorithms. As further contribution, we also release human annotated ground-truth vein pixel labels (required for training the networks) for a subset of two well known finger-vein databases used in this work, and a corresponding tool for further annotations.