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The overlapping effect and fusion protocols of data augmentation techniques in iris PAD

Fang, Meiling ; Damer, Naser ; Boutros, Fadi ; Kirchbuchner, Florian ; Kuijper, Arjan (2024)
The overlapping effect and fusion protocols of data augmentation techniques in iris PAD.
In: Machine Vision and Applications, 2022, 33 (1)
doi: 10.26083/tuprints-00023431
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: The overlapping effect and fusion protocols of data augmentation techniques in iris PAD
Language: English
Date: 12 March 2024
Place of Publication: Darmstadt
Year of primary publication: January 2022
Place of primary publication: Berlin; Heidelberg
Publisher: Springer
Journal or Publication Title: Machine Vision and Applications
Volume of the journal: 33
Issue Number: 1
Collation: 21 Seiten
DOI: 10.26083/tuprints-00023431
Corresponding Links:
Origin: Secondary publication DeepGreen

Iris Presentation Attack Detection (PAD) algorithms address the vulnerability of iris recognition systems to presentation attacks. With the great success of deep learning methods in various computer vision fields, neural network-based iris PAD algorithms emerged. However, most PAD networks suffer from overfitting due to insufficient iris data variability. Therefore, we explore the impact of various data augmentation techniques on performance and the generalizability of iris PAD. We apply several data augmentation methods to generate variability, such as shift, rotation, and brightness. We provide in-depth analyses of the overlapping effect of these methods on performance. In addition to these widely used augmentation techniques, we also propose an augmentation selection protocol based on the assumption that various augmentation techniques contribute differently to the PAD performance. Moreover, two fusion methods are performed for more comparisons: the strategy-level and the score-level combination. We demonstrate experiments on two fine-tuned models and one trained from the scratch network and perform on the datasets in the Iris-LivDet-2017 competition designed for generalizability evaluation. Our experimental results show that augmentation methods improve iris PAD performance in many cases. Our least overlap-based augmentation selection protocol achieves the lower error rates for two networks. Besides, the shift augmentation strategy also exceeds state-of-the-art (SoTA) algorithms on the Clarkson and IIITD-WVU datasets.

Uncontrolled Keywords: Iris presentation attack detection, Data augmentation, Deep learning
Identification Number: Artikel-ID: 8
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-234310
Additional Information:

Special Issue on 25th ICPR - Computer Vision, Robotics and Intelligent Systems

Classification DDC: 000 Generalities, computers, information > 004 Computer science
500 Science and mathematics > 510 Mathematics
Divisions: 20 Department of Computer Science > Fraunhofer IGD
20 Department of Computer Science > Mathematical and Applied Visual Computing
Date Deposited: 12 Mar 2024 13:26
Last Modified: 30 Apr 2024 09:39
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/23431
PPN: 517425572
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