  Biometric verification systems are deployed in various security-based
access-control applications that require user-friendly and reliable person
verification. Among the different biometric characteristics, fingervein
biometrics have been extensively studied owing to their reliable verification
performance. Furthermore, fingervein patterns reside inside the skin and are
not visible outside; therefore, they possess inherent resistance to
presentation attacks and degradation due to external factors. In this paper, we
introduce a novel fingervein verification technique using a convolutional
multihead attention network called VeinAtnNet. The proposed VeinAtnNet is
designed to achieve light weight with a smaller number of learnable parameters
while extracting discriminant information from both normal and enhanced
fingervein images. The proposed VeinAtnNet was trained on the newly constructed
fingervein dataset with 300 unique fingervein patterns that were captured in
multiple sessions to obtain 92 samples per unique fingervein. Extensive
experiments were performed on the newly collected dataset FV-300 and the
publicly available FV-USM and FV-PolyU fingervein dataset. The performance of
the proposed method was compared with five state-of-the-art fingervein
verification systems, indicating the efficacy of the proposed VeinAtnNet.
