# Transformer based Fingerprint Feature Extraction

arXiv: https://arxiv.org/abs/2209.03846 · HTML: https://mlsanity.com/paper/2209.03846/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 8 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [CvT: Introducing Convolutions to Vision Transformers](https://mlsanity.com/paper/2103.15808/) (2021)
- [TransFG: A Transformer Architecture for Fine-grained Recognition](https://mlsanity.com/paper/2103.07976/) (2021)
- [SiT: Self-supervised vIsion Transformer](https://mlsanity.com/paper/2104.03602/) (2021)
- [A Unified Model for Fingerprint Authentication and Presentation Attack Detection](https://mlsanity.com/paper/2104.03255/) (2021)

## Built on by (most cited first, 7 of 7)

- [AFR-Net: Attention-Driven Fingerprint Recognition Network](https://mlsanity.com/paper/2211.13897/) (2022)
- [IFViT: Interpretable Fixed-Length Representation for Fingerprint Matching via Vision Transformer](https://mlsanity.com/paper/2404.08237/) (2024)
- [Minutiae-Guided Fingerprint Embeddings via Vision Transformers](https://mlsanity.com/paper/2210.13994/) (2022)
- [Mobile Contactless Palmprint Recognition: Use of Multiscale, Multimodel Embeddings](https://mlsanity.com/paper/2401.08111/) (2024)
- [MRA-GNN: Minutiae Relation-Aware Model over Graph Neural Network for Fingerprint Embedding](https://mlsanity.com/paper/2307.16416/) (2023)
- [ViT Unified: Joint Fingerprint Recognition and Presentation Attack Detection](https://mlsanity.com/paper/2305.07602/) (2023)
- [FPBench: A Comprehensive Benchmark of Multimodal Large Language Models for Fingerprint Analysis](https://mlsanity.com/paper/2512.18073/) (2025)

## Abstract (arXiv)

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.