Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.01213 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917973861597184 |
|---|---|
| author | Adami, Banafsheh Karimian, Nima |
| author_facet | Adami, Banafsheh Karimian, Nima |
| contents | Although contactless fingerprints offer user comfort, they are more vulnerable to spoofing. The current solution for anti-spoofing in the area of contactless fingerprints relies on domain adaptation learning, limiting their generalization and scalability. To address these limitations, we introduce GRU-AUNet, a domain adaptation approach that integrates a Swin Transformer-based UNet architecture with GRU-enhanced attention mechanisms, a Dynamic Filter Network in the bottleneck, and a combined Focal and Contrastive Loss function. Trained in both genuine and spoof fingerprint images, GRU-AUNet demonstrates robust resilience against presentation attacks, achieving an average BPCER of 0.09\% and APCER of 1.2\% in the CLARKSON, COLFISPOOF, and IIITD datasets, outperforming state-of-the-art domain adaptation methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01213 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GRU-AUNet: A Domain Adaptation Framework for Contactless Fingerprint Presentation Attack Detection Adami, Banafsheh Karimian, Nima Computer Vision and Pattern Recognition Although contactless fingerprints offer user comfort, they are more vulnerable to spoofing. The current solution for anti-spoofing in the area of contactless fingerprints relies on domain adaptation learning, limiting their generalization and scalability. To address these limitations, we introduce GRU-AUNet, a domain adaptation approach that integrates a Swin Transformer-based UNet architecture with GRU-enhanced attention mechanisms, a Dynamic Filter Network in the bottleneck, and a combined Focal and Contrastive Loss function. Trained in both genuine and spoof fingerprint images, GRU-AUNet demonstrates robust resilience against presentation attacks, achieving an average BPCER of 0.09\% and APCER of 1.2\% in the CLARKSON, COLFISPOOF, and IIITD datasets, outperforming state-of-the-art domain adaptation methods. |
| title | GRU-AUNet: A Domain Adaptation Framework for Contactless Fingerprint Presentation Attack Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.01213 |