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Boosting Generalization with Adaptive Style Techniques for Fingerprint Liveness Detection

arXiv 2310.13573 ↗First posted 20 Oct 2023cs.CV cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 17 references that are in the map. First posted on arXiv 20 Oct 2023.

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References · 10

  1. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  2. 2020Knowledge Distillation: A Surveycited by 1,578
  3. 2018Reconciling modern machine learning practice and the bias-variance trade-offcited by 919
  4. 2017Deep Mutual Learningcited by 621
  5. 2020A Brief Review of Domain Adaptationcited by 287
  6. 2017Domain Adaptation for Visual Applications: A Comprehensive Surveycited by 127
  7. 2018A Modern Take on the Bias-Variance Tradeoff in Neural Networkscited by 64
  8. 2022AugLy: Data Augmentations for Robustnesscited by 34
  9. 2021CrossNorm and SelfNorm for Generalization under Distribution Shiftscited by 33
  10. 2023LivDet2023 -- Fingerprint Liveness Detection Competition: Advancing Generalizationcited by 3

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