Finger-UNet: A U-Net Based Multi-Task Architecture for Deep Fingerprint Enhancement

Ekta Gavas, Anoop Namboodiri

2023

Abstract

For decades, fingerprint recognition has been prevalent for security, forensics, and other biometric applications. However, the availability of good-quality fingerprints is challenging, making recognition difficult. Fingerprint images might be degraded with a poor ridge structure and noisy or less contrasting backgrounds. Hence, fingerprint enhancement plays a vital role in the early stages of the fingerprint recognition/verification pipeline. In this paper, we investigate and improvise the encoder-decoder style architecture and suggest intuitive modifications to U-Net to enhance low-quality fingerprints effectively. We investigate the use of Discrete Wavelet Transform (DWT) for fingerprint enhancement and use a wavelet attention module instead of max pooling which proves advantageous for our task. Moreover, we replace regular convolutions with depthwise separable convolutions, which significantly reduces the memory footprint of the model without degrading the performance. We also demonstrate that incorporating domain knowledge with fingerprint minutiae prediction task can improve fingerprint reconstruction through multi-task learning. Furthermore, we also integrate the orientation estimation task to propagate the knowledge of ridge orientations to enhance the performance further. We present the experimental results and evaluate our model on FVC 2002 and NIST SD302 databases to show the effectiveness of our approach compared to previous works.

Paper Citation

in Harvard Style

Gavas E. and Namboodiri A. (2023). Finger-UNet: A U-Net Based Multi-Task Architecture for Deep Fingerprint Enhancement. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 309-316. DOI: 10.5220/0011687400003417

in Bibtex Style

@conference{visapp23,
author={Ekta Gavas and Anoop Namboodiri},
title={Finger-UNet: A U-Net Based Multi-Task Architecture for Deep Fingerprint Enhancement},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={309-316},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011687400003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Finger-UNet: A U-Net Based Multi-Task Architecture for Deep Fingerprint Enhancement
SN - 978-989-758-634-7
AU - Gavas E.
AU - Namboodiri A.
PY - 2023
SP - 309
EP - 316
DO - 10.5220/0011687400003417
PB - SciTePress