Abstract
The concern of the safety of fingerprint authentication system is rising with its widely using for it is easy to be attacked by spoof (fake) fingerprints. Fake fingerprints are usually made of Ploy-Doh, silicon or other artifacts. So most current approaches rely on fingerprint liveness detection as main anti-spoofing mechanisms. Recently, researchers propose to use local feature descriptor for fingerprint liveness detection, but the results are still not satisfying the real world application requirement. Inspired by the newly trend of application of Deep Convolution Neural Network (DCNN) in computer vision field and its outstanding performance in face detection and image classification, we propose a novel fingerprint liveness detection method based on DCNN and voting strategy, which performs better than handcraft feature and optimize the process of feature extraction and classifier training simultaneously. The experimental results on the datasets of LivDet2011 and LivDet2013 show that the proposed algorithm has great improvement compare to the former state-of-the-art algorithm, and keep highly real-time performance at the same time.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Marcel, S., Mark, S.: Nixon: Handbook of Biometric Anti-Spoofing. Springer, New York (2014)
Gragnaniello, D., Poggi, G.: An Investigation of Local Descriptors for Biometric Spoofing Detection. J. IEEE Trans. Inf. Foren. Secur. 10(4), 849–863 (2015)
Maltoni, D., Maio, D., Jain, A.K., et al.: Handbook of Fingerprint Recognition, Springer (2009)
Schuckers, S.: Spoofing and anti-spoofing measures. Information Security Technical Report 7(4), 56–62 (2002)
Marcialis, G.L., Lewicke, A., Tan, B.: First international fingerprint liveness detection competition - LivDet 2009. In: 15th International Conference on Image Analysis and Processing, pp. 12-23, Vietri sul Mare, Italy (2009)
Yambay, D., Ghiani, L., Denti, P.: LivDet 2011 - fingerprint liveness detection competition 2011. In: 5th International Conference on Biometrics, pp. 208-215, New Delhi, India (2012)
Ghiani, L.,Yambay, D., Tocco, S.: LivDet 2013 fingerprint liveness detection competition 2013. In: 6th International Conference on Biometrics, pp. 1-6, Madrid, Spain (2013)
LeCun, Y., Boser, B., Denker, J.S., Henderson, D.: Back propagation applied to handwritten zip code recognition. Neural Computation (1989)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolution neural networks. In: 22th Annual Conference on Neural Information Processing Systems, pp. 1097-1105. IEEE Press, Columbus (2012)
Girshick, R., Donahue, J., Darrel, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 27th IEEE Conference on Computer Vision and Pattern Recognition, pp. 580-587. IEEE Press, Columbus (2014)
Zhang, N., Paluri, M., Ranzato, M., Darrell, T.: Panda: pose aligned networks for deep attribute modeling. In: 27th IEEE Conference on Computer Vision and Pattern Recognition, pp. 1637-1644. IEEE Press, Columbus (2014)
Galbally, J., Alonso-Fernandez, F., Fierrez, J.: A high performance fingerprint liveness detection method based on quality related features. J. Future Generation Computer Systems 28, 311–321 (2012)
Galbally, J. Alonso-Fernandez, F.: Fingerprint liveness detection based on quality measures. In: 2009 Int. Conference on Biometrics, Identity and Security, pp. 1-8. IEEE Press, Tampa (2009)
Kannala, J., Rahtu, E.: Bsif: Binarized statistical image features. In: 21st Int. Conference on Pattern Recognition, pp. 1363-1366. IEEE Press, Tuskuba (2012)
Ojala, T., Peitikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. J IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Ghiani, L., Marcialis, G.L., Roli, F.: Fingerprint liveness detection by local phase quantization. In: 21st Int. Conf. Pattern Recognit, pp. 537-540. IEEE Press, Tsukuba (2012)
Gragnaniello, D., Poggi, G., Sansone, G., Verdoliva, L.: Local contrast phase descriptor for fingerprint liveness detection. J. Pattern Recognit. 48(4), 1050–1058 (2015)
Ghiani, L., Hadid, A., Marcialis, G.: Fingerprint liveness detection using binarized statistical image feature. In: 6th IEEE Int. Conference on Biometrics: Theory Application and Systems, pp. 1-6. IEEE Press, Arlington (2013)
Nogueira, R.F., Alencar Lotufo, R.: Evaluting software-based fingerprint liveness detection using convolutional networks and local binary patterns. In: IEEE Workshop Biometric Meas. Syst. Secur. Med. Appl., pp. 22-29. IEEE Press, Rome (2014)
Sun, Y., Wang, X., Tang, X.: Hybrid deep learning for face verification. In: 2013 IEEE International Conference on Computer Vision, pp. 1489-1496. IEEE Press, Sydney (2013)
Bengio, Y.: Learning deep architectures for AI. Foundations and Trends(r) in Machine Learning 2(1), 1–127 (2009)
Wan, L., Zeiler, M., Zhang, S., Cun, Y.L., Fergus, R.: Regularization of neural networks using dropconnect. In: 30th International Conference on Machine Learning, pp. 1058-1066. Atlanta (2013)
Github, https://github.com/BVLC/caffe/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, C., Li, K., Wu, Z., Zhao, Q. (2015). A DCNN Based Fingerprint Liveness Detection Algorithm with Voting Strategy. In: Yang, J., Yang, J., Sun, Z., Shan, S., Zheng, W., Feng, J. (eds) Biometric Recognition. CCBR 2015. Lecture Notes in Computer Science(), vol 9428. Springer, Cham. https://doi.org/10.1007/978-3-319-25417-3_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-25417-3_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25416-6
Online ISBN: 978-3-319-25417-3
eBook Packages: Computer ScienceComputer Science (R0)