Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection

Open access

Abstract

Biometric systems have been widely considered as a synonym of security. However, in recent years, malicious people are violating them by presenting forged traits, such as gelatin fingers, to fool their capture sensors (spoofing attacks). To detect such frauds, methods based on traditional image descriptors have been developed, aiming liveness detection from the input data. However, due to their handcrafted approaches, most of them present low accuracy rates in challenging scenarios. In this work, we propose a novel method for fingerprint spoofing detection using the Deep Boltzmann Machines (DBM) for extraction of high-level features from the images. Such deep features are very discriminative, thus making complicated the task of forgery by attackers. Experiments show that the proposed method outperforms other state-of-the-art techniques, presenting high accuracy regarding attack detection.

[1] A. Jain, A. Ross and K. Nandakumar, Introduction to Biometrics, Springer, 2011.

[2] A Biniaz and A. Abbasi, Segmentation and edge detection based on modified ant colony optimization for iris image processing, Journal of Artificial Intelligence and Soft Computing Research (JAISCR), vol. 3, no. 2, 2013, pp. 133-141.

[3] D. Menotti, G. Chiachia, A. Pinto, W. Schwartz, H. Pedrini, A. Falcao and A. Rocha, Deep representations for iris, face, and fingerprint spoofing attack detection, IEEE Transactions on Information Forensics and Security, vol. 10, no. 4, 2015, pp. 864-879.

[4] L. Ghiani, V. Mura, S. Tocco, G. Marcialis, F. Roli, D. Yambay and S. Schuckers, LivDet 2013 fingerprint liveness detection competition, In: Proceedings of International Conference on Biometrics, 2013, pp. 1-6.

[5] G. Souza, D. Santos, R. Pires, A. Marana, J. Papa, Deep Boltzmann Machines for robust fingerprint spoofing attack detection, In: Proceedings of International Joint Conference on Neural Networks, 2017, pp. 1863-1870.

[6] R. Salakhutdinov and G. Hinton, Deep Boltzmann Machines, Technical Report, University of Toronto, 2009.

[7] G. Hinton, Training products of experts by minimizing Contrastive Divergence, Neural Computation, vol. 14, no. 2, 2002, pp.1771-1800.

[8] G. Hinton, Neural networks: tricks of the trade, Springer, Berlin, 2012.

[9] N. Ratha, J. Connel and R. Bolle, An analysis of minutiae matching strength, In: Proceedings of International Conference on Audio- and Video-Based Biometric Person Authentication, 2001, pp. 223-228.

[10] J. Galbally, J. Fierrez and J. Garcia, Vulnerabilities in biometric systems: attacks and recent advances in liveness detection, Database, vol. 1, no. 3, 2007, pp. 1-8.

[11] K. Patel, H. Han and A. Jain, Cross-database face antispoofing with robust feature representation, In: Proceedings of Chinese Conference on Biometric Recognition, 2016, pp. 611-619.

[12] V. Nair and G. Hinton, Implicit mixtures of Restricted Boltzmann Machines, Advances in Neural Information Processing Systems, vol. 21, 2009, pp. 1145-1152.

[13] D. MacKays, Information theory, inference and learning algorithms, Cambridge University Press, 2003.

[14] R. Salakhutdinov and H. Larochelle, Efficient learning of Deep Boltzmann Machines, Artificial Intelligence and Statistics, 2010, pp. 693-700.

[15] S. Kullback, Probability densities with given marginals, Annals of Mathematical Statistics, vol. 39, no. 4, 1968, pp. 1236-1243.

[16] I. Navon and D. Legler, Conjugate-gradient methods for large-scale minimization in Meteorology, Monthly Weather Review, American Meteorological Society, vol. 115, 1987, pp. 1479-1502.

[17] Y. LeCun, L. Bottou, G. Orr and K. Müller, Efficient Backprop., Springer-Verlag, United Kingdom, 1998.

[18] C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, vol. 20, no. 3, 1995, pp. 273-297.

[19] H. Hotelling, Analysis of a complex of statistical variables into principal components, Journal of Educational Psychology, vol. 24, 1933, pp. 417-441.

Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

Journal Information

CiteScore 2017: 5.00

SCImago Journal Rank (SJR) 2017: 0.492
Source Normalized Impact per Paper (SNIP) 2017: 2.813

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 69 69 59
PDF Downloads 32 32 28