GRU-AUNet: A Domain Adaptation Framework for Contactless Fingerprint Presentation Attack Detection
This addresses anti-spoofing for contactless fingerprint systems, offering improved generalization and scalability over existing methods, though it appears incremental as it builds on domain adaptation techniques.
The paper tackles the problem of spoofing in contactless fingerprints by proposing GRU-AUNet, a domain adaptation framework that integrates Swin Transformer-based UNet with GRU-enhanced attention, achieving an average BPCER of 0.09% and APCER of 1.2% across multiple datasets.
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.