# Deep Age-Invariant Fingerprint Segmentation System

arXiv: https://arxiv.org/abs/2303.03341 · HTML: https://mlsanity.com/paper/2303.03341/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 6 Mar 2023.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Deep Slap Fingerprint Segmentation for Juveniles and Adults](https://mlsanity.com/paper/2110.04067/) (2021)
- [Sensor-invariant Fingerprint ROI Segmentation Using Recurrent Adversarial Learning](https://mlsanity.com/paper/2107.01361/) (2021)

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

- [Deep Learning-Based Approaches for Contactless Fingerprints Segmentation and Extraction](https://mlsanity.com/paper/2311.15163/) (2023)
- [TipSegNet: Fingertip Segmentation in Contactless Fingerprint Imaging](https://mlsanity.com/paper/2501.05076/) (2025)
- [Pixel-Level Pavement Distress Assessment Using Instance Segmentation](https://mlsanity.com/paper/2605.26095/) (2026)
- [SLAPBench: Benchmarking Multimodal Large Language Models for Four-Finger SLAP Fingerprint Verification](https://mlsanity.com/paper/2607.15517/) (2026)

## Abstract (arXiv)

Fingerprint-based identification systems achieve higher accuracy when a slap containing multiple fingerprints of a subject is used instead of a single fingerprint. However, segmenting or auto-localizing all fingerprints in a slap image is a challenging task due to the different orientations of fingerprints, noisy backgrounds, and the smaller size of fingertip components. The presence of slap images in a real-world dataset where one or more fingerprints are rotated makes it challenging for a biometric recognition system to localize and label the fingerprints automatically. Improper fingerprint localization and finger labeling errors lead to poor matching performance. In this paper, we introduce a method to generate arbitrary angled bounding boxes using a deep learning-based algorithm that precisely localizes and labels fingerprints from both axis-aligned and over-rotated slap images. We built a fingerprint segmentation model named CRFSEG (Clarkson Rotated Fingerprint segmentation Model) by updating the previously proposed CFSEG model which was based on traditional Faster R-CNN architecture [21]. CRFSEG improves upon the Faster R-CNN algorithm with arbitrarily angled bounding boxes that allow the CRFSEG to perform better in challenging slap images. After training the CRFSEG algorithm on a new dataset containing slap images collected from both adult and children subjects, our results suggest that the CRFSEG model was invariant across different age groups and can handle over-rotated slap images successfully. In the Combined dataset containing both normal and rotated images of adult and children subjects, we achieved a matching accuracy of 97.17%, which outperformed state-of-the-art VeriFinger (94.25%) and NFSEG segmentation systems (80.58%).