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Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction

Abstract: 

Latent fingerprint reconstruction is a vital preprocessing step for its identification. This task is very challenging due to not only existing complicated degradation patterns but also its scarcity of paired training data. To address these challenges, we propose a novel generative adversarial network (GAN) based data augmentation scheme to improve such reconstruction. It translates the abundant clean fingerprints to their corresponding latent ones, only exploiting a small-scale latent dataset and an unpaired large-scale clean dataset, from which a large-scale paired clean-latent augmentation set is built for the reconstruction task. Specifically, our method models the distribution of the latent degradation patterns into a Gaussian one and generates latent fingerprints based on the sampled degradation patterns and clean fingerprints. Besides, we develop an auxiliary training procedure to stabilize training and further disentangle ridge structures and degradation patterns by regressing a latent fingerprint from its latent representation and its corresponding binarized fingerprint. Boosted by the proposed data augmentation, our reconstruction shows significant improvements in the visual evaluation and fingerprint identification performance.

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Paper Details

Authors:
Ying Xu, Yi Wang, Jiajun Liang, Yong Jiang
Submitted On:
15 May 2020 - 1:16am
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Type:
Presentation Slides
Event:
Presenter's Name:
Ying Xu
Paper Code:
1263
Document Year:
2020
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ICASSP1263.pdf

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[1] Ying Xu, Yi Wang, Jiajun Liang, Yong Jiang, "Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5335. Accessed: Sep. 19, 2020.
@article{5335-20,
url = {http://sigport.org/5335},
author = {Ying Xu; Yi Wang; Jiajun Liang; Yong Jiang },
publisher = {IEEE SigPort},
title = {Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction},
year = {2020} }
TY - EJOUR
T1 - Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction
AU - Ying Xu; Yi Wang; Jiajun Liang; Yong Jiang
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5335
ER -
Ying Xu, Yi Wang, Jiajun Liang, Yong Jiang. (2020). Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction. IEEE SigPort. http://sigport.org/5335
Ying Xu, Yi Wang, Jiajun Liang, Yong Jiang, 2020. Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction. Available at: http://sigport.org/5335.
Ying Xu, Yi Wang, Jiajun Liang, Yong Jiang. (2020). "Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction." Web.
1. Ying Xu, Yi Wang, Jiajun Liang, Yong Jiang. Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5335