  Fingerprint feature extraction is a task that is solved using either a global
or a local representation. State-of-the-art global approaches use heavy deep
learning models to process the full fingerprint image at once, which makes the
corresponding approach memory intensive. On the other hand, local approaches
involve minutiae based patch extraction, multiple feature extraction steps and
an expensive matching stage, which make the corresponding approach time
intensive. However, both these approaches provide useful and sometimes
exclusive insights for solving the problem. Using both approaches together for
extracting fingerprint representations is semantically useful but quite
inefficient. Our convolutional transformer based approach with an in-built
minutiae extractor provides a time and memory efficient solution to extract a
global as well as a local representation of the fingerprint. The use of these
representations along with a smart matching process gives us state-of-the-art
performance across multiple databases. The project page can be found at
https://saraansh1999.github.io/global-plus-local-fp-transformer.
