Transformer based Fingerprint Feature Extraction

TL;DR

This paper introduces a convolutional transformer method that efficiently combines global and local fingerprint features, achieving state-of-the-art accuracy while reducing memory and time costs.

Contribution

It proposes a novel convolutional transformer model with an integrated minutiae extractor for combined global and local fingerprint feature extraction.

Findings

Achieves state-of-the-art performance on multiple fingerprint databases.

Reduces memory and time consumption compared to existing methods.

Effectively combines global and local fingerprint representations.

Abstract

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.