Fang, Meiling ; Damer, Naser ; Boutros, Fadi ; Kirchbuchner, Florian ; Kuijper, Arjan (2022)
The overlapping effect and fusion protocols of data augmentation techniques in iris PAD.
In: Machine Vision and Applications, 33 (1)
doi: 10.1007/s00138-021-01256-9
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Fang, Meiling ; Damer, Naser ; Boutros, Fadi ; Kirchbuchner, Florian ; Kuijper, Arjan |
| Art des Eintrags: | Bibliographie |
| Titel: | The overlapping effect and fusion protocols of data augmentation techniques in iris PAD |
| Sprache: | Englisch |
| Publikationsjahr: | Januar 2022 |
| Verlag: | Springer |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Machine Vision and Applications |
| Jahrgang/Volume einer Zeitschrift: | 33 |
| (Heft-)Nummer: | 1 |
| DOI: | 10.1007/s00138-021-01256-9 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | 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. |
| Freie Schlagworte: | Biometrics, Spoofing attacks, Iris recognition, Machine learning, Deep learning |
| Zusätzliche Informationen: | Erstveröffentlichung |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 10 Dez 2021 08:54 |
| Letzte Änderung: | 14 Mär 2024 14:04 |
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| Suche nach Titel in: | TUfind oder in Google |
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The overlapping effect and fusion protocols of data augmentation techniques in iris PAD. (deposited 12 Mär 2024 13:26)
- The overlapping effect and fusion protocols of data augmentation techniques in iris PAD. (deposited 10 Dez 2021 08:54) [Gegenwärtig angezeigt]
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