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Erschienen in:
A Brief Review of Image Denoising Algorithms and Beyond Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-net Based Convolutional Neural Networks

verfasst von : Sukesh Adiga V, Jayanthi Sivaswamy

Erschienen in: Inpainting and Denoising Challenges

Verlag: Springer International Publishing

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Abstract

Fingerprint is a common biometric used for authentication and verification of an individual. These images are degraded when fingers are wet, dirty, dry or wounded and due to the failure of the sensors, etc. The extraction of the fingerprint from a degraded image requires denoising and inpainting. We propose to address these problems with an end-to-end trainable Convolutional Neural Network based architecture called FPD-M-net, by posing the fingerprint denoising and inpainting problem as a segmentation (foreground) task. Our architecture is based on the M-net with a change: structure similarity loss function, used for better extraction of the fingerprint from the noisy background. Our method outperforms the baseline method and achieves an overall 3rd rank in the Chalearn LAP Inpainting Competition Track 3—Fingerprint Denoising and Inpainting, ECCV 2018.

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Vorheriges Kapitel U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting
Nächstes Kapitel Iterative Application of Autoencoders for Video Inpainting and Fingerprint Denoising
Fußnoten
Literatur
1.
Cao, K., Jain, A.K.: Latent orientation field estimation via convolutional neural network. In: 2015 International Conference on Biometrics (ICB), pp. 349–356. IEEE (2015)
2.
Chen, C., Feng, J., Zhou, J.: Multi-scale dictionaries based fingerprint orientation field estimation. In: 2016 International Conference on Biometrics (ICB), pp. 1–8. IEEE (2016)
3.
Feng, J., Zhou, J., Jain, A.K.: Orientation field estimation for latent fingerprint enhancement. IEEE transactions on pattern analysis and machine intelligence 35(4), 925–940 (2013)CrossRef
4.
Greenberg, S., Aladjem, M., Kogan, D.: Fingerprint image enhancement using filtering techniques. Real-time Imaging 8(3), 227–236 (2002)CrossRef
5.
Hong, L., Wan, Y., Jain, A.: Fingerprint image enhancement: Algorithm and performance evaluation. IEEE Transactions on pattern analysis and machine intelligence 20(8), 777–789 (1998)CrossRef
6.
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
7.
Jain, A.K., Hong, L., Pankanti, S., Bolle, R.: An identity-authentication system using fingerprints. Proceedings of the IEEE 85(9), 1365–1388 (1997)CrossRef
8.
Lee, C.Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: Deeply-supervised nets. In: Artificial Intelligence and Statistics, pp. 562–570 (2015)
9.
Li, J., Feng, J., Kuo, C.C.J.: Deep convolutional neural network for latent fingerprint enhancement. Signal Processing: Image Communication 60, 52–63 (2018)
10.
Maio, D., Maltoni, D., Cappelli, R., Wayman, J.L., Jain, A.K.: Fvc2000: Fingerprint verification competition. IEEE Transactions on Pattern Analysis & Machine Intelligence (3), 402–412 (2002)CrossRef
11.
Mehta, R., Sivaswamy, J.: M-net: A convolutional neural network for deep brain structure segmentation. In: Proc. of 14th International Symposium on Biomedical Imaging (ISBI), pp. 437–440. IEEE (2017)
12.
Nguyen, D.L., Cao, K., Jain, A.K.: Robust minutiae extractor: Integrating deep networks and fingerprint domain knowledge. In: 2018 International Conference on Biometrics (ICB), pp. 9–16. IEEE (2018)
13.
Rahmes, M., Allen, J.D., Elharti, A., Tenali, G.B.: Fingerprint reconstruction method using partial differential equation and exemplar-based inpainting methods. In: Biometrics Symposium, pp. 1–6. IEEE (2007)
14.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention (MICCAI), pp. 234–241. Springer (2015)
15.
Sahasrabudhe, M., Namboodiri, A.M.: Fingerprint enhancement using unsupervised hierarchical feature learning. In: Proceedings of Indian Conference on Computer Vision Graphics and Image Processing, p. 2. ACM (2014)
16.
Salakhutdinov, R., Tenenbaum, J.B., Torralba, A.: Learning with hierarchical-deep models. IEEE transactions on pattern analysis and machine intelligence 35(8), 1958–1971 (2013)CrossRef
17.
Sergio, E., et al.: Chalearn looking at people: Inpainting and denoising challenges. Challenges in Machine Learning (CiML) (2019)
18.
Singh, K., Kapoor, R., Nayar, R.: Fingerprint denoising using ridge orientation based clustered dictionaries. Neurocomputing 167, 418–423 (2015)CrossRef
19.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15(1), 1929–1958 (2014)MathSciNetMATH
20.
Srivastava, R.K., Greff, K., Schmidhuber, J.: Highway networks. arXiv preprint arXiv:1505.00387 (2015)
21.
Svoboda, J., Monti, F., Bronstein, M.M.: Generative convolutional networks for latent fingerprint reconstruction. In: 2017 IEEE International Joint Conference on Biometrics (IJCB), pp. 429–436. IEEE (2017)
22.
Tang, Y., Gao, F., Feng, J., Liu, Y.: Fingernet: An unified deep network for fingerprint minutiae extraction. In: IEEE International Joint Conference on Biometrics (IJCB), pp. 108–116. IEEE (2017)
23.
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004)CrossRef
24.
Wang, Z., Simoncelli, E., Bovik, A., et al.: Multi-scale structural similarity for image quality assessment. In: ASILOMAR CONFERENCE ON SIGNALS SYSTEMS AND COMPUTERS, vol. 2, pp. 1398–1402. IEEE (2003)
25.
Wu, C., Shi, Z., Govindaraju, V.: Fingerprint image enhancement method using directional median filter. In: Biometric Technology for Human Identification, vol. 5404, pp. 66–76. International Society for Optics and Photonics (2004)
26.
Xie, J., Xu, L., Chen, E.: Image denoising and inpainting with deep neural networks. In: Advances in neural information processing systems, pp. 341–349 (2012)
27.
Yang, X., Feng, J., Zhou, J.: Localized dictionaries based orientation field estimation for latent fingerprints. IEEE transactions on pattern analysis and machine intelligence 36(5), 955–969 (2014)CrossRef
28.
Zhao, H., Gallo, O., Frosio, I., Kautz, J.: Loss functions for image restoration with neural networks. IEEE Transactions on Computational Imaging 3(1), 47–57 (2017)CrossRef
Metadaten
Titel
FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-net Based Convolutional Neural Networks
verfasst von
Sukesh Adiga V
Jayanthi Sivaswamy
Copyright-Jahr
2019
Buch
Inpainting and Denoising Challenges

Print ISBN: 978-3-030-25613-5

Electronic ISBN: 978-3-030-25614-2

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-25614-2_4

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