Marattukalam, FelixAbdulla, Waleed H.Swain, AkshyaBrömme, ArslanBusch, ChristophDamer, NaserDantcheva, AntitzaGomez-Barrero, MartaRaja, KiranRathgeb, ChristianSequeira, AnaUhl, Andreas2021-10-042021-10-042021978-3-88579-709-8https://dl.gi.de/handle/20.500.12116/37467The use of deep learning methods to extract vascular biometric patterns from the palm surface has been of interest among researchers in recent years. In many biometric recognition tasks, there is a limit in the number of training samples. This is because of limited vein biometric databases being available for research. This restricts the application of deep learning methods to design algorithms that can effectively identify or authenticate people for vein recognition. This paper proposes an architecture using Siamese neural network structure for few shot palm vein verification. The proposed network uses images from both the palms and consists of two sub-nets that share weights to identify a person. The architecture’s performance was tested on the HK PolyU multi spectral palm vein database with limited samples. The results suggest that the method is effective since it has 91.9% precision, 91.1% recall, 92.2% specificity, 91.5% F1-Score, and 90.5% accuracy values.enPalm Vein VerificationbiometricsSiamese neural networkfew- shot learningText/Conference Paper1617-5468