Enhanced Segmentation-CNN based Finger-Vein Recognition by Joint Training with Automatically Generated and Manual Labels

Deep learning techniques are nowadays the leading approaches to solve complex machine learning and pattern recognition problems. For the first time, we utilize state-of-the-art semantic segmentation CNNs to extract vein patterns from near-infrared finger imagery and use them as the actual vein features in biometric finger-vein recognition. In this context, beside investigating the impact of training data volume, we propose a training model based on automatically generated labels, to improve the recognition performance of the resulting vein structures compared to (i) network training using manual labels only, and compared to (ii) well established classical recognition techniques relying on publicly available software. Proposing this model we also take a crucial step in reducing the amount of manually annotated labels required to train networks, whose generation is extremely time consuming and error-prone. As further contribution, we also release human annotated ground-truth vein pixel labels (required for training the networks) for a subset of a well known finger-vein database used in this work, and a corresponding tool for further annotations.