Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.14665 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913240302223360 |
|---|---|
| author | Medvedev, Iurii Gonçalves, Nuno |
| author_facet | Medvedev, Iurii Gonçalves, Nuno |
| contents | Recent advancements in deep learning have revolutionized technology and security measures, necessitating robust identification methods. Biometric approaches, leveraging personalized characteristics, offer a promising solution. However, Face Recognition Systems are vulnerable to sophisticated attacks, notably face morphing techniques, enabling the creation of fraudulent documents. In this study, we introduce a novel quadruplet loss function for increasing the robustness of face recognition systems against morphing attacks. Our approach involves specific sampling of face image quadruplets, combined with face morphs, for network training. Experimental results demonstrate the efficiency of our strategy in improving the robustness of face recognition networks against morphing attacks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14665 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quadruplet Loss For Improving the Robustness to Face Morphing Attacks Medvedev, Iurii Gonçalves, Nuno Computer Vision and Pattern Recognition Recent advancements in deep learning have revolutionized technology and security measures, necessitating robust identification methods. Biometric approaches, leveraging personalized characteristics, offer a promising solution. However, Face Recognition Systems are vulnerable to sophisticated attacks, notably face morphing techniques, enabling the creation of fraudulent documents. In this study, we introduce a novel quadruplet loss function for increasing the robustness of face recognition systems against morphing attacks. Our approach involves specific sampling of face image quadruplets, combined with face morphs, for network training. Experimental results demonstrate the efficiency of our strategy in improving the robustness of face recognition networks against morphing attacks. |
| title | Quadruplet Loss For Improving the Robustness to Face Morphing Attacks |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.14665 |