# FingerNet: Pushing The Limits of Fingerprint Recognition Using Convolutional Neural Network

arXiv: https://arxiv.org/abs/1907.12956 · HTML: https://mlsanity.com/paper/1907.12956/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2019.

## Built on by (most cited first, 4 of 4)

- [GaitPT: Skeletons Are All You Need For Gait Recognition](https://mlsanity.com/paper/2308.10623/) (2023)
- [An Unsupervised Deep-Learning Method for Fingerprint Classification: the CCAE Network and the Hybrid Clustering Strategy](https://mlsanity.com/paper/2109.05526/) (2021)
- [On the Adversarial Inversion of Deep Biometric Representations](https://mlsanity.com/paper/2304.05561/) (2023)
- [Enhancing Fingerprint Recognition Systems: Comparative Analysis of Biometric Authentication Algorithms and Techniques for Improved Accuracy and Reliability](https://mlsanity.com/paper/2412.14404/) (2024)

## Abstract (arXiv)

Fingerprint recognition has been utilized for cellphone authentication, airport security and beyond. Many different features and algorithms have been proposed to improve fingerprint recognition. In this paper, we propose an end-to-end deep learning framework for fingerprint recognition using convolutional neural networks (CNNs) which can jointly learn the feature representation and perform recognition. We train our model on a large-scale fingerprint recognition dataset, and improve over previous approaches in terms of accuracy. Our proposed model is able to achieve a very high recognition accuracy on a well-known fingerprint dataset. We believe this framework can be widely used for biometrics recognition tasks, making more scalable and accurate systems possible. We have also used a visualization technique to highlight the important areas in an input fingerprint image, that mostly impact the recognition results.