Improving Presentation Attack Detection Classification Accuracy: Novel Approaches Incorporating Facial Expressions, Backdrops, and Data Augmentation
In the evolving landscape of biometric authentication, the integrity of face recognition systems against sophisticated presentation attacks (PAD) is paramount. This study set out to elevate the detection capabilities of PAD systems by ingeniously integrating a teacher–student learning framework with...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/7/2166 |
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| author | Tayyaba Riaz Adeel Anjum Madiha Haider Syed Semeen Rehman |
| author_facet | Tayyaba Riaz Adeel Anjum Madiha Haider Syed Semeen Rehman |
| author_sort | Tayyaba Riaz |
| collection | DOAJ |
| description | In the evolving landscape of biometric authentication, the integrity of face recognition systems against sophisticated presentation attacks (PAD) is paramount. This study set out to elevate the detection capabilities of PAD systems by ingeniously integrating a teacher–student learning framework with cutting-edge PAD methodologies. Our approach is anchored in the realization that conventional PAD models, while effective to a degree, falter in the face of novel, unseen attack vectors and complex variations. As a solution, we suggest a novel architecture where a teacher network, trained on a comprehensive dataset embodying a broad spectrum of attacks and genuine instances, distills knowledge to a student network. The student network, specifically focusing on the nuanced detection of genuine samples in target domains, leverages minimalist yet representative attack data. This methodology is enriched by incorporating facial expressions, dynamic backgrounds, and adversarially generated attack simulations, aiming to mimic the sophisticated techniques attackers might employ. Through rigorous experimentation and validation on benchmark datasets, our results manifested a substantial leap in classification accuracy, particularly for those samples that have traditionally posed a challenge. The newly proposed model, which can not only effectively outperform existing PAD solutions, but also achieve admirable flexibility and applicability to novel attack scenarios, truly demonstrates the power of the proposed teacher–student framework. This paves the way for improved security and trustworthiness in the area of face recognition systems and the deployment of biometric technologies. |
| format | Article |
| id | doaj-art-fe873bdb52df4bd68e707db8a0347c3e |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-fe873bdb52df4bd68e707db8a0347c3e2025-08-20T03:08:56ZengMDPI AGSensors1424-82202025-03-01257216610.3390/s25072166Improving Presentation Attack Detection Classification Accuracy: Novel Approaches Incorporating Facial Expressions, Backdrops, and Data AugmentationTayyaba Riaz0Adeel Anjum1Madiha Haider Syed2Semeen Rehman3Institute of Information Technology, Quaid-e-Azam University Islamabad, Islamabad 45320, PakistanInstitute of Information Technology, Quaid-e-Azam University Islamabad, Islamabad 45320, PakistanInstitute of Information Technology, Quaid-e-Azam University Islamabad, Islamabad 45320, PakistanInstitute of Computer Technology, Technical University of Vienna (TU Wien), 1040 Vienna, AustriaIn the evolving landscape of biometric authentication, the integrity of face recognition systems against sophisticated presentation attacks (PAD) is paramount. This study set out to elevate the detection capabilities of PAD systems by ingeniously integrating a teacher–student learning framework with cutting-edge PAD methodologies. Our approach is anchored in the realization that conventional PAD models, while effective to a degree, falter in the face of novel, unseen attack vectors and complex variations. As a solution, we suggest a novel architecture where a teacher network, trained on a comprehensive dataset embodying a broad spectrum of attacks and genuine instances, distills knowledge to a student network. The student network, specifically focusing on the nuanced detection of genuine samples in target domains, leverages minimalist yet representative attack data. This methodology is enriched by incorporating facial expressions, dynamic backgrounds, and adversarially generated attack simulations, aiming to mimic the sophisticated techniques attackers might employ. Through rigorous experimentation and validation on benchmark datasets, our results manifested a substantial leap in classification accuracy, particularly for those samples that have traditionally posed a challenge. The newly proposed model, which can not only effectively outperform existing PAD solutions, but also achieve admirable flexibility and applicability to novel attack scenarios, truly demonstrates the power of the proposed teacher–student framework. This paves the way for improved security and trustworthiness in the area of face recognition systems and the deployment of biometric technologies.https://www.mdpi.com/1424-8220/25/7/2166sparse learningdata augmentationone-class domain adaptationadversarial trainingknowledge distillationdecision-making accuracy |
| spellingShingle | Tayyaba Riaz Adeel Anjum Madiha Haider Syed Semeen Rehman Improving Presentation Attack Detection Classification Accuracy: Novel Approaches Incorporating Facial Expressions, Backdrops, and Data Augmentation Sensors sparse learning data augmentation one-class domain adaptation adversarial training knowledge distillation decision-making accuracy |
| title | Improving Presentation Attack Detection Classification Accuracy: Novel Approaches Incorporating Facial Expressions, Backdrops, and Data Augmentation |
| title_full | Improving Presentation Attack Detection Classification Accuracy: Novel Approaches Incorporating Facial Expressions, Backdrops, and Data Augmentation |
| title_fullStr | Improving Presentation Attack Detection Classification Accuracy: Novel Approaches Incorporating Facial Expressions, Backdrops, and Data Augmentation |
| title_full_unstemmed | Improving Presentation Attack Detection Classification Accuracy: Novel Approaches Incorporating Facial Expressions, Backdrops, and Data Augmentation |
| title_short | Improving Presentation Attack Detection Classification Accuracy: Novel Approaches Incorporating Facial Expressions, Backdrops, and Data Augmentation |
| title_sort | improving presentation attack detection classification accuracy novel approaches incorporating facial expressions backdrops and data augmentation |
| topic | sparse learning data augmentation one-class domain adaptation adversarial training knowledge distillation decision-making accuracy |
| url | https://www.mdpi.com/1424-8220/25/7/2166 |
| work_keys_str_mv | AT tayyabariaz improvingpresentationattackdetectionclassificationaccuracynovelapproachesincorporatingfacialexpressionsbackdropsanddataaugmentation AT adeelanjum improvingpresentationattackdetectionclassificationaccuracynovelapproachesincorporatingfacialexpressionsbackdropsanddataaugmentation AT madihahaidersyed improvingpresentationattackdetectionclassificationaccuracynovelapproachesincorporatingfacialexpressionsbackdropsanddataaugmentation AT semeenrehman improvingpresentationattackdetectionclassificationaccuracynovelapproachesincorporatingfacialexpressionsbackdropsanddataaugmentation |