Over the past decade, deep learning has transformed mul-
timedia processing and, consequently, biometrics, due to its ability to learn
meaningful features directly from raw data. The effectiveness of this learning
depends largely on the availability of sufficient data. In this study, we explore
fingerprint recognition as a deep learning classification task, constrained by the
availability of only a few examples per class for
prediction
