Fast Fingerprint Classification with Deep Neural Networks

Daniel Michelsanti, Andreea-Daniela Ene, Yanis Guichi, Rares Stef, Kamal Nasrollahi, Thomas B. Moeslund

Abstract

Reducing the number of comparisons in automated fingerprint identification systems is essential when dealing with a large database. Fingerprint classification allows to achieve this goal by dividing fingerprints into several categories, but it presents still some challenges due to the large intra-class variations and the small inter-class variations. The vast majority of the previous methods uses global characteristics, in particular the orientation image, as features of a classifier. This makes the feature extraction stage highly dependent on preprocessing techniques and usually computationally expensive. In this work we evaluate the performance of two pre-trained convolutional neural networks fine-tuned on the NIST SD4 benchmark database. The obtained results show that this approach is comparable with other results in the literature, with the advantage of a fast feature extraction stage.

References

  1. Candela, G. T., Grother, P. J., Watson, C. I., Wilkinson, R., and Wilson, C. L. (1995). PCASYS - A patternlevel classification automation system for fingerprints. NIST technical report NISTIR, 5647.
  2. Cao, K., Pang, L., Liang, J., and Tian, J. (2013). Fingerprint classification by a hierarchical classifier. Pattern Recognition, 46(12):3186-3197.
  3. Cappelli, R. and Maio, D. (2004). The state of the art in fingerprint classification. In Automatic Fingerprint Recognition Systems, pages 183-205. Springer.
  4. Cappelli, R., Maio, D., and Maltoni, D. (1999). Fingerprint classification based on multi-space KL. In Proceedings Workshop on Automatic Identification Advances Technologies (AutoID99), pages 117-120.
  5. Cappelli, R., Maio, D., Maltoni, D., and Nanni, L. (2003). A two-stage fingerprint classification system. In Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications, pages 95-99. ACM.
  6. Chatfield, K., Simonyan, K., Vedaldi, A., and Zisserman, A. (2014). Return of the devil in the details: Delving deep into convolutional nets. In Proceedings of British Machine Vision Conference, pages 1-11.
  7. Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T. (2014). DeCAF: A deep convolutional activation feature for generic visual recognition. 32:647-655.
  8. Galar, M., Derrac, J., Peralta, D., Triguero, I., Paternain, D., Lopez-Molina, C., García, S., Benítez, J. M., Pagola, M., Barrenechea, E., et al. (2015). A survey of fingerprint classification part I: Taxonomies on feature extraction methods and learning models. Knowledgebased systems, 81:76-97.
  9. Hertel, L., Barth, E., Kaster, T., and Martinetz, T. (2015). Deep convolutional neural networks as generic feature extractors. In Neural Networks (IJCNN), 2015 International Joint Conference on, pages 1-4. IEEE.
  10. Ioffe, S. and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. 37:448-456.
  11. Krizhevsky, A. and Hinton, G. (2009). Learning multiple layers of features from tiny images. Technical report, University of Toronto.
  12. Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097-1105.
  13. LeCun, Y., Cortes, C., and Burges, C. J. (1998). The MNIST database of handwritten digits. http://yann. lecun.com/exdb/mnist. Accessed: 01-03-2016.
  14. Li, J., Yau, W.-Y., and Wang, H. (2008). Combining singular points and orientation image information for fingerprint classification. Pattern Recognition, 41(1):353-366.
  15. Maltoni, D., Maio, D., Jain, A., and Prabhakar, S. (2009). Handbook of fingerprint recognition. Springer Science & Business Media.
  16. Mayhew, S. (2015). History of biometrics. http://www. biometricupdate.com/201501/history-of-biometrics. 01/09/2016.
  17. Nogueira, R., Lotufo, R., and Campos Machado, R. (2016). Fingerprint liveness detection using convolutional neural networks. IEEE Transactions on Information Forensics and Security, 11(6):1206-1213.
  18. Park, C. H. and Park, H. (2005). Fingerprint classification using fast Fourier transform and nonlinear discriminant analysis. Pattern Recognition, 38(4):495-503.
  19. Razavian, A., Azizpour, H., Sullivan, J., and Carlsson, S. (2014). CNN features off-the-shelf: an astounding baseline for recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 806-813.
  20. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and LeCun, Y. (2013). OverFeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229.
  21. Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
  22. Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1):1929-1958.
  23. Tan, X., Bhanu, B., and Lin, Y. (2003). Learning features for fingerprint classification. In International Conference on Audio-and Video-Based Biometric Person Authentication, pages 318-326. Springer.
  24. Tan, X., Bhanu, B., and Lin, Y. (2005). Fingerprint classification based on learned features. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 35(3):287-300.
  25. Vedaldi, A. and Lenc, K. (2015). MatConvNet: Convolutional neural networks for MATLAB. In Proceedings of the 23rd Annual ACM Conference on Multimedia Conference, pages 689-692. ACM.
  26. Watson, C. I. and Wilson, C. L. (1992a). NIST Special Database 4. https://www.nist.gov/srd/nist-specialdatabase-4. Accessed: 01-09-2016.
  27. Watson, C. I. and Wilson, C. L. (1992b). NIST Special Database 9. https://www.nist.gov/srd/nist-specialdatabase-9. Accessed: 01-09-2016.
  28. Watson, C. I. and Wilson, C. L. (1993). NIST Special Database 14. https://www.nist.gov/srd/nist-specialdatabase-14. Accessed: 01-09-2016.
  29. Zhang, Q. and Yan, H. (2004). Fingerprint classification based on extraction and analysis of singularities and pseudo ridges. Pattern Recognition, 37(11):2233- 2243.
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Paper Citation


in Harvard Style

Michelsanti D., Ene A., Guichi Y., Stef R., Nasrollahi K. and Moeslund T. (2017). Fast Fingerprint Classification with Deep Neural Networks . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-226-4, pages 202-209. DOI: 10.5220/0006116502020209


in Bibtex Style

@conference{visapp17,
author={Daniel Michelsanti and Andreea-Daniela Ene and Yanis Guichi and Rares Stef and Kamal Nasrollahi and Thomas B. Moeslund},
title={Fast Fingerprint Classification with Deep Neural Networks},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={202-209},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006116502020209},
isbn={978-989-758-226-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)
TI - Fast Fingerprint Classification with Deep Neural Networks
SN - 978-989-758-226-4
AU - Michelsanti D.
AU - Ene A.
AU - Guichi Y.
AU - Stef R.
AU - Nasrollahi K.
AU - Moeslund T.
PY - 2017
SP - 202
EP - 209
DO - 10.5220/0006116502020209