Efficient Deep Learning Architectures for Face Presentation Attack Detection
Resumo
Biometric systems are common in our everyday life: from our mobile devices to huge surveillance systems. Despite the higher difficulty to circumvent biometric applications, criminals are simulating traits such as face or fingerprints of valid users (presentation attacks - PA), in order to fool the security applications. Deep neural networks have obtained state-of-theart results in PA detection. However, in many cases, they are computationally expensive, being not feasible in environments with hardware restrictions, such as mobile ones. In this sense, we propose efficient deep learning architectures for PA detection, especially for face recognition systems, able to be trained and deployed even when there is low computational power available.Referências
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Akhtar, Z. and Foresti, G. L. (2016). Face spoof attack recognition using discriminative image patches. Journal of Electrical and Computer Engineering, 16.
Atoum, Y., Liu, Y., Jourabloo, A., and Liu, X. (2017). Face anti-spoofing using patch and depth-based CNNs. In Proceedings of International Joint Conference on Biometrics.
Boulkenafet, Z., Komulainen, J., and Hadid, A. (2015). Face anti-spoofing based on color texture analysis. In Anais da International Conference on Image Processing, pages 2636–2640. IEEE.
Chingovska, I., Anjos, A., and Marcel, S. (2012). On the effectiveness of local binary patterns in face anti-spoofing. In Anais da International Conference of Biometrics Special Interest Group (BIOSIG), pages 1–7.
Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3):273–297.
Hinton, G. E. (2002). Training products of experts by minimizing contrastive divergence. In Neural Computation, pages 1771– 1800.
Hinton, G. E. (2012). A practical guide to training Restricted Boltzmann Machines. In Montavon, G., Orr, G. B., and Muller, K. R., ¨ editors, Neural Networks: Tricks of the trade. Springer, United States.
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. CoRR, abs/1704.04861.
ISO (2016). ISO/IEC 30107 - Presentation attack detection.
Jain, A. K., Ross, A., and Nandakumar, K. (2011). Introduction to Biometrics. Springer, United States.
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R. B., Guadarrama, S., and Darrell, T. (2014). Caffe: Convolutional architecture for fast feature embedding. CoRR, abs/1408.5093.
Kingma, D. and Ba, J. (2015). Adam: a method for stochastic optimization. In Anais da International Conference for Learning Representations.
Krizhevsky, A., Sutskever, I., and Hinton, G. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, pages 1106–1114.
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. In Proceedings of the IEEE, pages 2278–2324.
Li, L., Feng, X., Boulkenafet, Z., Xia, Z., Li, M., and Hadid, A. (2016). An original face anti-spoofing approach using partial convolutional neural network. In Proc. of International Conference on Image Processing Theory, Tools and Applications, pages 1–6.
Lucena, O., Junior, A., Moia, V., Souza, R., Valle, E., and de Alencar Lotufo, R. (2017). Transfer learning using convolutional neural networks for face anti-spoofing. In Anais da International Conference on Image Analysis and Recognition, pages 27–34.
Ojala, T., Pietikainen, M., and M ¨ aenp ¨ a¨a, T. (2002). Mul- ¨ tiresolution gray-scale and rotation invariant texture classification with Local Binary Patterns. In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 24, pages 971–987
Parkhi, O. M., Vedaldi, A., and Zisserman, A. (2015). Deep face recognition. In Anais da British Machine Vision Conference.
Schwartz, W., Rocha, A., and Pedrini, H. (2011). Face spoofing detection through Partial Least Squares and Low-Level Descriptors. In Int. Joint Conf. on Biometrics, United States. IEEE.
Souza, G. B., Santos, D. F. S., Pires, R. G., Marana, A. N., and Papa, J. P. (2017). Efficient transfer learning for robust face spoofing detection. In Anais do Iberoamerican Congress on Pattern Recognition.
Tan, X., Li, Y., Liu, J., and Jiang, L. (2010). Face liveness detection from a single image with sparse low rank bilinear discriminative model. In Anais da European Conference on Computer Vision, pages 504– 517.
Tang, Y., Salakhutdinov, R., and Hinton, G. E. (2012). Robust Boltzmann Machines for recognition and denoising. In Proc. of Conference on Computer Vision and Pattern Recognition, United States. IEEE.
Wang, Z., Tang, X., Luo, W., and Gao, S. (2018). Face aging with identity-preserved conditional generative adversarial networks. In Anais da Conference on Computer Vision and Pattern Recognition.
Yang, J., Lei, Z., and Li, S. Z. (2014). Learn Convolutional Neural Network for face anti-spoofing. CoRR, abs/1408.5601.
Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., and Li, S. (2012). A face antispoofing database with diverse attacks. In Proc. of International Conference on Biometrics, Unites States. IEEE.
Publicado
07/11/2020
Como Citar
DE SOUZA, Gustavo Botelho; PAPA, João Paulo; MARANA, Aparecido Nilceu.
Efficient Deep Learning Architectures for Face Presentation Attack Detection. In: WORKSHOP DE TESES E DISSERTAÇÕES - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 33. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
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p. 112-118.
DOI: https://doi.org/10.5753/sibgrapi.est.2020.12992.
