Analysis of the synthetic periocular iris images for robust Presentation Attacks Detection algorithms
Abstract The LivDet‐2020 competition focuses on Presentation Attacks Detection (PAD) algorithms, has still open problems, mainly unknown attack scenarios. It is crucial to enhance PAD methods. This can be achieved by augmenting the number of Presentation Attack Instruments (PAI) and Bona fide (genui...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-07-01
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| Series: | IET Biometrics |
| Online Access: | https://doi.org/10.1049/bme2.12084 |
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| author | Jose Maureira Juan E. Tapia Claudia Arellano Christoph Busch |
| author_facet | Jose Maureira Juan E. Tapia Claudia Arellano Christoph Busch |
| author_sort | Jose Maureira |
| collection | DOAJ |
| description | Abstract The LivDet‐2020 competition focuses on Presentation Attacks Detection (PAD) algorithms, has still open problems, mainly unknown attack scenarios. It is crucial to enhance PAD methods. This can be achieved by augmenting the number of Presentation Attack Instruments (PAI) and Bona fide (genuine) images used to train such algorithms. Unfortunately, the capture and creation of PAI and even the capture of Bona fide images are sometimes complex to achieve. The generation of synthetic images with Generative Adversarial Networks (GAN) algorithms may help and has shown significant improvements in recent years. This paper presents a benchmark of GAN methods to achieve a novel synthetic PAI from a small set of periocular near‐infrared images. The best PAI was obtained using StyleGAN2, and it was tested using the best PAD algorithm from the LivDet‐2020. The synthetic PAI was able to fool such an algorithm. As a result, all images were classified as Bona fide. A MobileNetV2 was trained using the synthetic PAI as a new class to achieve a more robust PAD. The resulting PAD was able to classify 96.7% of synthetic images as attacks. BPCER10 was 0.24%. Such results demonstrated the need for PAD algorithms to be constantly updated and trained with synthetic images. |
| format | Article |
| id | doaj-art-83fbc7ab65bd4f358a0e06e5011a23b9 |
| institution | OA Journals |
| issn | 2047-4938 2047-4946 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj-art-83fbc7ab65bd4f358a0e06e5011a23b92025-08-20T02:09:07ZengWileyIET Biometrics2047-49382047-49462022-07-0111434335410.1049/bme2.12084Analysis of the synthetic periocular iris images for robust Presentation Attacks Detection algorithmsJose Maureira0Juan E. Tapia1Claudia Arellano2Christoph Busch3TOC Biometrics, R&D Center SR‐226 Santiago Chileda/sec‐Biometrics and Internet Security Research Group Hochschule Darmstadt Darmstadt GermanyUniversidad Adolfo Ibañez Santiago Chileda/sec‐Biometrics and Internet Security Research Group Hochschule Darmstadt Darmstadt GermanyAbstract The LivDet‐2020 competition focuses on Presentation Attacks Detection (PAD) algorithms, has still open problems, mainly unknown attack scenarios. It is crucial to enhance PAD methods. This can be achieved by augmenting the number of Presentation Attack Instruments (PAI) and Bona fide (genuine) images used to train such algorithms. Unfortunately, the capture and creation of PAI and even the capture of Bona fide images are sometimes complex to achieve. The generation of synthetic images with Generative Adversarial Networks (GAN) algorithms may help and has shown significant improvements in recent years. This paper presents a benchmark of GAN methods to achieve a novel synthetic PAI from a small set of periocular near‐infrared images. The best PAI was obtained using StyleGAN2, and it was tested using the best PAD algorithm from the LivDet‐2020. The synthetic PAI was able to fool such an algorithm. As a result, all images were classified as Bona fide. A MobileNetV2 was trained using the synthetic PAI as a new class to achieve a more robust PAD. The resulting PAD was able to classify 96.7% of synthetic images as attacks. BPCER10 was 0.24%. Such results demonstrated the need for PAD algorithms to be constantly updated and trained with synthetic images.https://doi.org/10.1049/bme2.12084 |
| spellingShingle | Jose Maureira Juan E. Tapia Claudia Arellano Christoph Busch Analysis of the synthetic periocular iris images for robust Presentation Attacks Detection algorithms IET Biometrics |
| title | Analysis of the synthetic periocular iris images for robust Presentation Attacks Detection algorithms |
| title_full | Analysis of the synthetic periocular iris images for robust Presentation Attacks Detection algorithms |
| title_fullStr | Analysis of the synthetic periocular iris images for robust Presentation Attacks Detection algorithms |
| title_full_unstemmed | Analysis of the synthetic periocular iris images for robust Presentation Attacks Detection algorithms |
| title_short | Analysis of the synthetic periocular iris images for robust Presentation Attacks Detection algorithms |
| title_sort | analysis of the synthetic periocular iris images for robust presentation attacks detection algorithms |
| url | https://doi.org/10.1049/bme2.12084 |
| work_keys_str_mv | AT josemaureira analysisofthesyntheticperiocularirisimagesforrobustpresentationattacksdetectionalgorithms AT juanetapia analysisofthesyntheticperiocularirisimagesforrobustpresentationattacksdetectionalgorithms AT claudiaarellano analysisofthesyntheticperiocularirisimagesforrobustpresentationattacksdetectionalgorithms AT christophbusch analysisofthesyntheticperiocularirisimagesforrobustpresentationattacksdetectionalgorithms |