Boosting Generalization with Adaptive Style Techniques for Fingerprint Liveness Detection
This work addresses the problem of improving accuracy and generalization in fingerprint liveness detection for security applications, though it appears incremental as it builds on existing methods like style transfer.
The paper tackled the problem of fingerprint liveness detection by introducing a feature extraction technique and a recognition system, achieving first place in the LivDet 2023 Fingerprint Representation Challenge and second place in the LivDet 2023 Liveness Detection in Action with 94.68% accuracy.
We introduce a high-performance fingerprint liveness feature extraction technique that secured first place in LivDet 2023 Fingerprint Representation Challenge. Additionally, we developed a practical fingerprint recognition system with 94.68% accuracy, earning second place in LivDet 2023 Liveness Detection in Action. By investigating various methods, particularly style transfer, we demonstrate improvements in accuracy and generalization when faced with limited training data. As a result, our approach achieved state-of-the-art performance in LivDet 2023 Challenges.