# A Contactless Fingerprint Recognition System

arXiv: https://arxiv.org/abs/2108.09048 · HTML: https://mlsanity.com/paper/2108.09048/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 20 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Advancements in Image Classification using Convolutional Neural Network](https://mlsanity.com/paper/1905.03288/) (2019)

## Built on by (most cited first, 3 of 3)

- [Finger-NestNet: Interpretable Fingerphoto Verification on Smartphone using Deep Nested Residual Network](https://mlsanity.com/paper/2212.05884/) (2022)
- [TinySiamese Network for Biometric Analysis](https://mlsanity.com/paper/2307.00578/) (2023)
- [G-MSGINet: A Grouped Multi-Scale Graph-Involution Network for Contactless Fingerprint Recognition](https://mlsanity.com/paper/2505.08233/) (2025)

## Abstract (arXiv)

Fingerprints are one of the most widely explored biometric traits. Specifically, contact-based fingerprint recognition systems reign supreme due to their robustness, portability and the extensive research work done in the field. However, these systems suffer from issues such as hygiene, sensor degradation due to constant physical contact, and latent fingerprint threats. In this paper, we propose an approach for developing a contactless fingerprint recognition system that captures finger photo from a distance using an image sensor in a suitable environment. The captured finger photos are then processed further to obtain global and local (minutiae-based) features. Specifically, a Siamese convolutional neural network (CNN) is designed to extract global features from a given finger photo. The proposed system computes matching scores from CNN-based features and minutiae-based features. Finally, the two scores are fused to obtain the final matching score between the probe and reference fingerprint templates. Most importantly, the proposed system is developed using the Nvidia Jetson Nano development kit, which allows us to perform contactless fingerprint recognition in real-time with minimum latency and acceptable matching accuracy. The performance of the proposed system is evaluated on an in-house IITI contactless fingerprint dataset (IITI-CFD) containing 105train and 100 test subjects. The proposed system achieves an equal-error-rate of 2.19% on IITI-CFD.