Linortner, MichaelUhl, AndreasBrömme, ArslanBusch, ChristophDamer, NaserDantcheva, AntitzaGomez-Barrero, MartaRaja, KiranRathgeb, ChristianSequeira, AnaUhl, Andreas2021-10-042021-10-042021978-3-88579-709-8https://dl.gi.de/handle/20.500.12116/37462Vein recognition usually uses binary features, but besides deep learning-based approaches key-point and minutiae-based ones started to become popular as well. Statistical measures for vein minutiae points, like spatial point distribution, have not been investigated in literature so far. In this work the number of vein minutiae points and their spatial distribution is analyzed in relation to recognition accuracy. The goal is to initiate a discussion on statistical behavior of vein minutiae points and deriving possible quality measures for vein minutiae point sets.enVein recognitionvein minutiae distributionspatial point distributionText/Conference Paper1617-5468