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Enhancement-Driven Pretraining for Robust Fingerprint Representation Learning (2402.10847v1)

Published 16 Feb 2024 in cs.CV

Abstract: Fingerprint recognition stands as a pivotal component of biometric technology, with diverse applications from identity verification to advanced search tools. In this paper, we propose a unique method for deriving robust fingerprint representations by leveraging enhancement-based pre-training. Building on the achievements of U-Net-based fingerprint enhancement, our method employs a specialized encoder to derive representations from fingerprint images in a self-supervised manner. We further refine these representations, aiming to enhance the verification capabilities. Our experimental results, tested on publicly available fingerprint datasets, reveal a marked improvement in verification performance against established self-supervised training techniques. Our findings not only highlight the effectiveness of our method but also pave the way for potential advancements. Crucially, our research indicates that it is feasible to extract meaningful fingerprint representations from degraded images without relying on enhanced samples.

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References (44)
  1. Fingerprint Identification Technology, pages 22–61. Springer London, London.
  2. Cappelli, R. (2004). Sfinge : an approach to synthetic fingerprint generation.
  3. Minutia cylinder-code: A new representation and matching technique for fingerprint recognition. IEEE transactions on pattern analysis and machine intelligence, 32(12):2128–2141.
  4. Mcc: A baseline algorithm for fingerprint verification in fvc-ongoing. In 2010 11th International Conference on Control Automation Robotics & Vision, pages 19–23. IEEE.
  5. Fingerprint image reconstruction from standard templates. IEEE transactions on pattern analysis and machine intelligence, 29(9):1489–1503.
  6. Unsupervised learning of visual features by contrasting cluster assignments.
  7. Fast algorithm for point pattern matching: invariant to translations, rotations and scale changes. Pattern recognition, 30(2):311–320.
  8. Big self-supervised models are strong semi-supervised learners.
  9. Improved baselines with momentum contrastive learning.
  10. Fingerprint image enhancement using stft analysis. In International Conference on Pattern Recognition and Image Analysis, pages 20–29. Springer.
  11. Fingerprint minutiae extraction using deep learning. In 2017 IEEE International Joint Conference on Biometrics (IJCB). IEEE.
  12. Cnnai: a convolution neural network-based latent fingerprint matching using the combination of nearest neighbor arrangement indexing. Frontiers in Robotics and AI, 7:113.
  13. Learning a fixed-length fingerprint representation. IEEE transactions on pattern analysis and machine intelligence, 43(6):1981–1997.
  14. Nist special database 302: Nail to nail fingerprint challenge.
  15. Finger-UNet: A u-net based multi-task architecture for deep fingerprint enhancement. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. SCITEPRESS - Science and Technology Publications.
  16. Fingerprint image enhancement using filtering techniques. Real-Time Imaging, 8(3):227–236.
  17. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33:21271–21284.
  18. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In Proceedings of the thirteenth international conference on artificial intelligence and statistics, pages 297–304. JMLR Workshop and Conference Proceedings.
  19. Fingerprint image enhancement: Algorithm and performance evaluation. IEEE transactions on pattern analysis and machine intelligence, 20(8):777–789.
  20. On-line fingerprint verification. IEEE transactions on pattern analysis and machine intelligence, 19(4):302–314.
  21. Fingerprint matching using minutiae and texture features. In Proceedings 2001 International Conference on Image Processing (Cat. No. 01CH37205), volume 3, pages 282–285. IEEE.
  22. A survey on contrastive self-supervised learning. Technologies, 9(1):2.
  23. Self-supervised visual feature learning with deep neural networks: A survey. IEEE transactions on pattern analysis and machine intelligence, 43(11):4037–4058.
  24. New enhancement algorithm for fingerprint images. In 2002 International Conference on Pattern Recognition, volume 3, pages 879–882. IEEE.
  25. Latent fingerprint enhancement via multi-scale patch based sparse representation. IEEE Transactions on Information Forensics and Security, 10(1):6–15.
  26. Automatic segmentation and enhancement of latent fingerprints using deep nested unets. IEEE Transactions on Information Forensics and Security, 16:1709–1719.
  27. Self-supervised learning: Generative or contrastive. IEEE transactions on knowledge and data engineering, 35(1):857–876.
  28. Fvc2000: Fingerprint verification competition. IEEE transactions on pattern analysis and machine intelligence, 24(3):402–412.
  29. Fvc2002: Second fingerprint verification competition. In 2002 International Conference on Pattern Recognition, volume 3. IEEE.
  30. Fvc2004: Third fingerprint verification competition. In Zhang, D. and Jain, A. K., editors, Biometric Authentication, pages 1–7, Berlin, Heidelberg. Springer Berlin Heidelberg.
  31. Fingerprint Matching, pages 167–233. Springer London, London.
  32. Fingerprint Sensing, pages 63–114. Springer International Publishing, Cham.
  33. Robust minutiae extractor: Integrating deep networks and fingerprint domain knowledge. In 2018 International Conference on Biometrics (ICB), pages 9–16. IEEE.
  34. Automatic differentiation in pytorch.
  35. Latent fingerprint enhancement based on denseunet. In 2019 International Conference on Biometrics (ICB), pages 1–6. IEEE.
  36. Improved image restoration using wavelet-based denoising and fourier-based deconvolution. In 2008 51st Midwest Symposium on Circuits and Systems, pages 249–252. IEEE.
  37. A real-time matching system for large fingerprint databases. IEEE transactions on pattern analysis and machine intelligence, 18(8):799–813.
  38. Sentence-bert: Sentence embeddings using siamese bert-networks.
  39. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer.
  40. Algorithm for enhancing fingerprint images. Electronics letters, 18(28):1720–1721.
  41. Fingernet: An unified deep network for fingerprint minutiae extraction. In 2017 IEEE International Joint Conference on Biometrics (IJCB), pages 108–116. IEEE.
  42. An Introduction to Biometric Authentication Systems, pages 1–20. Springer London, London.
  43. Improved method for extraction of fingerprint features. In Second International Conference on Image and Graphics, volume 4875, pages 552–558. SPIE.
  44. Zaeri, N. (2011). Minutiae-based fingerprint extraction and recognition. Biometrics.

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