Deep Age-Invariant Fingerprint Segmentation System

TL;DR

This paper introduces CRFSEG, a deep learning-based fingerprint segmentation system that accurately localizes and labels fingerprints in slap images regardless of orientation, improving recognition accuracy across age groups.

Contribution

The paper presents CRFSEG, an improved deep learning model with arbitrarily angled bounding boxes for better fingerprint segmentation in challenging slap images, outperforming existing systems.

Findings

CRFSEG achieves 97.17% matching accuracy on combined datasets.

CRFSEG outperforms VeriFinger and NFSEG in accuracy.

The model is invariant across different age groups and orientations.

Abstract

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%).