Presentation Attack Detection using iris periocular visual spectrum images
In this work, we analyse the comparison between using the periocular area instead of the full face area for Presentation Attack Detection (PAD) in the visual spectrum (RGB). The analysis was carried out by evaluating the performance of five Convolutional Neural Networks (CNN) using both facial and p...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Imaging |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimag.2024.1478783/full |
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| author | Andrés Valenzuela Juan E. Tapia Violeta Chang Christoph Busch |
| author_facet | Andrés Valenzuela Juan E. Tapia Violeta Chang Christoph Busch |
| author_sort | Andrés Valenzuela |
| collection | DOAJ |
| description | In this work, we analyse the comparison between using the periocular area instead of the full face area for Presentation Attack Detection (PAD) in the visual spectrum (RGB). The analysis was carried out by evaluating the performance of five Convolutional Neural Networks (CNN) using both facial and periocular iris images for PAD with two different attack instruments. Additionally, we improved the CNN results by integrating the ArcFace loss function instead of the traditional categorical cross-entropy loss, highlighting that the ArcFace function enhances the performance of the models for both regions of interest, facial and iris periocular areas. We conducted Binary and Multiclass comparisons, followed by cross-database validation to assess the generalization capabilities of the trained models. Our study also addresses some of the current challenges in PAD research, such as the limited availability of high-quality face datasets in the desired spectrum (RGB), which impacts the quality of Presentation Attack Instruments (PAI) examples used in training and evaluation. Our goal was to address the challenge of detecting Iris periocular presentation attacks by leveraging the ArcFace function. The results demonstrate the effectiveness of our approach and provide valuable insights for improving PAD systems using periocular areas in the visual spectrum. |
| format | Article |
| id | doaj-art-aaee1cffe055472fa28cdd99c5a0a1a0 |
| institution | Kabale University |
| issn | 2813-3315 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Imaging |
| spelling | doaj-art-aaee1cffe055472fa28cdd99c5a0a1a02024-12-18T17:55:38ZengFrontiers Media S.A.Frontiers in Imaging2813-33152024-12-01310.3389/fimag.2024.14787831478783Presentation Attack Detection using iris periocular visual spectrum imagesAndrés Valenzuela0Juan E. Tapia1Violeta Chang2Christoph Busch3Departamento de Ingeniería Informática, Universidad de Santiago de Chile, Santiago, Chileda/sec-Biometrics and Internet Security Research Group, Hochschule Darmstadt, Darmstadt, GermanyDepartamento de Ingeniería Informática, Universidad de Santiago de Chile, Santiago, Chileda/sec-Biometrics and Internet Security Research Group, Hochschule Darmstadt, Darmstadt, GermanyIn this work, we analyse the comparison between using the periocular area instead of the full face area for Presentation Attack Detection (PAD) in the visual spectrum (RGB). The analysis was carried out by evaluating the performance of five Convolutional Neural Networks (CNN) using both facial and periocular iris images for PAD with two different attack instruments. Additionally, we improved the CNN results by integrating the ArcFace loss function instead of the traditional categorical cross-entropy loss, highlighting that the ArcFace function enhances the performance of the models for both regions of interest, facial and iris periocular areas. We conducted Binary and Multiclass comparisons, followed by cross-database validation to assess the generalization capabilities of the trained models. Our study also addresses some of the current challenges in PAD research, such as the limited availability of high-quality face datasets in the desired spectrum (RGB), which impacts the quality of Presentation Attack Instruments (PAI) examples used in training and evaluation. Our goal was to address the challenge of detecting Iris periocular presentation attacks by leveraging the ArcFace function. The results demonstrate the effectiveness of our approach and provide valuable insights for improving PAD systems using periocular areas in the visual spectrum.https://www.frontiersin.org/articles/10.3389/fimag.2024.1478783/fullbiometricsPresentation Attack Detectionfaceirisperiocular |
| spellingShingle | Andrés Valenzuela Juan E. Tapia Violeta Chang Christoph Busch Presentation Attack Detection using iris periocular visual spectrum images Frontiers in Imaging biometrics Presentation Attack Detection face iris periocular |
| title | Presentation Attack Detection using iris periocular visual spectrum images |
| title_full | Presentation Attack Detection using iris periocular visual spectrum images |
| title_fullStr | Presentation Attack Detection using iris periocular visual spectrum images |
| title_full_unstemmed | Presentation Attack Detection using iris periocular visual spectrum images |
| title_short | Presentation Attack Detection using iris periocular visual spectrum images |
| title_sort | presentation attack detection using iris periocular visual spectrum images |
| topic | biometrics Presentation Attack Detection face iris periocular |
| url | https://www.frontiersin.org/articles/10.3389/fimag.2024.1478783/full |
| work_keys_str_mv | AT andresvalenzuela presentationattackdetectionusingirisperiocularvisualspectrumimages AT juanetapia presentationattackdetectionusingirisperiocularvisualspectrumimages AT violetachang presentationattackdetectionusingirisperiocularvisualspectrumimages AT christophbusch presentationattackdetectionusingirisperiocularvisualspectrumimages |