Bros, VictorKotwal, KetanMarcel, SébastienBrömme, ArslanBusch, ChristophDamer, NaserDantcheva, AntitzaGomez-Barrero, MartaRaja, KiranRathgeb, ChristianSequeira, AnaUhl, Andreas2021-10-042021-10-042021978-3-88579-709-8https://dl.gi.de/handle/20.500.12116/37463The field of Vascular Biometric Recognition has drawn a lot of attention recently with the emergence of new computer vision techniques. The different methods using Deep Learning involve a new understanding of deeper features from the vascular network. The specific architecture of the veins needs complex model capable of comprehending the vascular pattern. In this paper, we present an image enhancement method using Deep Convolutional Neural Network. For this task, a residual convolutional auto-encoder architecture has been trained in a supervised way to enhance the vein patterns in near-infrared images. The method has been evaluated on several databases with promising results on the UTFVP database as a main result. In including the model as a preprocessing in the biometric pipelines of recognition for finger vein patterns, the error rate has been reduced from 2.1% to 1.0%.enFinger Vein RecognitionDeep Residual Convolutional Auto-EncoderVein EnhancementText/Conference Paper1617-5468