  Contactless and online palmprint identfication offers improved user
convenience, hygiene, user-security and is highly desirable in a range of
applications. This technical report details an accurate and generalizable deep
learning-based framework to detect and recognize humans using contactless
palmprint images in the wild. Our network is based on fully convolutional
network that generates deeply learned residual features. We design a
soft-shifted triplet loss function to more effectively learn discriminative
palmprint features. Online palmprint identification also requires a contactless
palm detector, which is adapted and trained from faster-R-CNN architecture, to
detect palmprint region under varying backgrounds. Our reproducible
experimental results on publicly available contactless palmprint databases
suggest that the proposed framework consistently outperforms several classical
and state-of-the-art palmprint recognition methods. More importantly, the model
presented in this report offers superior generalization capability, unlike
other popular methods in the literature, as it does not essentially require
database-specific parameter tuning, which is another key advantage over other
methods in the literature.
