Open Journal Systems

Deep Discriminative Restricted Boltzmann Machine (DDRBM) for Robust Face Spoofing Detection

Gustavo Botelho de Souza, Joao Paulo Papa, Aparecido Nilceu Marana

Abstract

Biometrics emerged as a robust solution for security systems. Despite, nowadays criminals are developing techniques to accurately simulate biometric traits of valid users, process known as spoofing attack, in order to circumvent the biometric applications. Face is among the main biometric characteristics, being extremely convenient for users given its non-intrusive capture by means of digital cameras. However, face recognition systems are the ones that most suffer with spoofing attacks since such cameras, in general, can be easily fooled with common printed photographs. In this sense, countermeasure techniques should be developed and integrated to the traditional face recognition systems in order to prevent such frauds. Among the main neural networks for face spoofing detection is the discriminative Restricted Boltzmann Machine (RBM) which, besides of efficiency, achieves great results in attack detection by learning the distributions of real and fake facial images. However, it is known that deeper neural networks present better accuracy results in many tasks. In this context, we propose a novel model called Deep Discriminative Restricted Boltzmann Machine (DDRBM) applied to face spoofing detection. Results on the NUAA dataset show a significative improvement in performance when compared to the accuracy rates of a traditional discriminative RBM on attack detection.

Keywords

Face spoofing detection; Biometrics; Deep Discriminative Restricted Boltzmann Machine; Restricted Boltzmann Machines; Deep Learning.

Full Text:

PDF

References

References

Jain, A K, Ross A, Nandakumar, K. Introduction to Biometrics. New York: Springer, 2011.

Jain et al. Biometrics: a grand challenge. Proceedings of the International Conference on Pattern Recognition; 2004. p. 935-942

Menotti et al. Deep representations for iris, face and fingerprint spoofing detection. IEEE Transactions on Information Forensics and Security 2015; 10(4):864-879.

Silva, M V, Marana, A N, Paulino, A A. On the importance of using high resolution images, third level features and sequence of images for fingerprint spoofing detection. Proceedings of the International Conference on Acoustics, Speech and Signal Processing; 2015. p. 1807-1811.

Tan et al. Face liveness detection from a single image with sparse low rank bilinear discriminative model. Proceedings of the European Conference on Computer Vision; 2010. p. 504-517, 2010.

Ojala, T, Pietikäinen, M, Harwood, D. A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 1996; 29(1):51-59.

Hinton, G. Training products of experts by minimizing contrastive divergence. Neural Computation 2002; 14(8):1711-1800.

Montavon, G, Orr, G, Müller, K. Neural Networks: Tricks of the Trade. Heidelberg: Springer, 2012.

Souza, G B, Marana, A N, Papa, J P. Detecção de ataques a sistemas de reconhecimento facial: uma abordagem baseada nas Máquinas de Boltzmann Restritas. Proceedings of the Regional Meeting on Applied and Computacional Mathmatics; 2017. p. 465-467.

Souza, G B, Santos, D, Gonçalves, R, Papa, J P, Marana, A N. Boltzmann Machines for robust fingerprint spoofing attack detection. Proceedings of the International Joint Conference on Neural Networks; 2017. p. 1863-1870.

Ojala, T, Pietikäinen, M, Maenpää, T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002; 24(7):971-987.

Chingovska, I, Anjos, A, Marcel, S. On the effectiveness of Local Binary Patterns in face anti-spoofing. Proceedings of BIOSIG; 2012.

Ackley, D, Hinton, G, Sejnowski, T. A learning algorithm for Boltzmann Machines. Cognitive Science 1985; 9(1):147-169.

Rumelhart, D, McClelland, J. Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Cambridge: MIT Press, 1986.

Tang, Y, Salakhutdinov, R, Hinton, G. Robust Boltzmann Machines for recognition and denoising. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2012.

Nair, V, Hinton, G. Implicit mixtures of restricted Boltzmann machines. Proceedings of the Conference on Neural Information Processing Systems; 2008.

Salomon, D. Data Compression - The Complete Reference. New York: Springer, 1998.


DOI: http://dx.doi.org/10.18063/phci.v1i3.893
(83 Abstract Views, 57 PDF Downloads)

Refbacks

  • There are currently no refbacks.


Copyright (c) 2018 Gustavo Botelho de Souza, Joao Paulo Papa, Aparecido Nilceu Marana

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.