Abstract
Face recognition has recently become widespread in security applications. Although advancing technology has improved the performance of these systems, they are still prone to various attacks, including spoofing. The inherent feature extraction capability of machine learning techniques and deep neural networks has facilitated more accurate performance in spoofing detection. However, challenges still remain in the generalisation of these methods. One significant challenge is training dataset limitation in terms of size and variance. This paper investigates how different train/test ratios and variance in training data affect model performance with the NUAA dataset for spoofing detection. We show how using different splits of this dataset results in different models with different performances. We also open up new research directions by demonstrating how the problem of generalisation can be neatly demonstrated with an existing manageable dataset.
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References
Souza, L., Oliveira, L., Pamplona, M., Papa, J.: How far did we get in face spoofing detection? Eng. Appl. Artif. Intell. 1(72), 368–81 (2018)
Galbally, J., Marcel, S., Fierrez, J.: Biometric antispoofing methods: a survey in face recognition. IEEE Access 18(2), 1530–52 (2014)
Ramachandra, R., Busch, C.: Presentation attack detection methods for face recognition systems: a comprehensive survey. ACM Comput. Surveys (CSUR). 50(1), 1–37 (2017)
Singh, R., Agarwal, A., Singh, M., Nagpal, S., Vatsa, M.: On the robustness of face recognition algorithms against attacks and bias. In: Proceedings of the AAAI Conference on Artificial Intelligence 3 April 2020, vol. 34, No. 09, pp. 13583–13589 (2020)
George, A., Mostaani, Z., Geissenbuhler, D., Nikisins, O., Anjos, A., Marcel, S.: Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Trans. Inf. Forensics Security 14(15), 42–55 (2019)
Tan, X., Li, Y., Liu, J., Jiang, L., Face liveness detection from a single image with sparse low rank bilinear discriminative model. In: European Conference on Computer Vision, vol. 5, pp. 504–517. Springer, Berlin, Heidelberg (2010)
Fahn, C.S., Lee, C.P., Wu, M.L.: A cross-dataset evaluation of anti-face-spoofing methods using random forests and convolutional neural networks. In: Proceedings of the 2019 2nd Artificial Intelligence and Cloud Computing Conference, 21 December 2019, pp. 89–96 (2019)
Li, J., Zhang, X., Zhang, Y., Wang, H., Yang, F.: Face liveness detection based on multiple feature descriptors. In: 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI), 21 November 2019, pp. 1–5. IEEE (2019)
Hasan, M.R., Mahmud, S.H., Li, X.Y.: Face anti-spoofing using texture-based techniques and filtering methods. In: J. Phys. Conf. Ser. 1229(1), 012044. IOP Publishing (2019)
Yılmaz, A.G., Turhal, U., Nabiyev, V.V.: Effect of feature selection with meta-heuristic optimization methods on face spoofing detection. J. Mod. Technol. Eng. 5(1), 48–59 (2020)
Satapathy, Ashutosh, Livingston, L.M.Jenila: A lite convolutional neural network built on permuted Xceptio-inception and Xceptio-reduction modules for texture based facial liveness recognition. Multimedia Tools Appl. 80(7), 10441–10472 (2020)
Parveen, S., Ahmad, S.M., Abbas, N.H., Adnan, W.A., Hanafi, M., Naeem, N.: Face liveness detection using dynamic local ternary pattern (DLTP). Computers 5(2), 10 (2016)
Koshy, R., Mahmood, A.: Optimizing deep CNN architectures for face liveness detection. Entropy 21(4), 423 (2019)
Beham, M.P., Roomi, S.M., Jebina, H., Kavitha, M.: Face spoofing detection using binary gradient orientation pattern with deep neural network. In: 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR), 27 December 2017, pp. 1–6. IEEE (2017)
Alotaibi, A., Mahmood, A.: Deep face liveness detection based on nonlinear diffusion using convolution neural network. Sign. Image Video Process. 11(4), 713–720 (2016)
Wang, S.Y., Yang, S.H., Chen, Y.P., Huang, J.W.: Face liveness detection based on skin blood flow analysis. Symmetry 9(12), 305 (2017)
Beham, M.P., Roomi, S.M.M.: Anti-spoofing enabled face recognition based on aggregated local weighted gradient orientation. Sign. Image Video Process. 12(3), 531–538 (2017)
Määttä, J., Hadid, A., Pietikäinen, M.: Face spoofing detection from single images using micro-texture analysis. In: 2011 International Joint Conference on Biometrics (IJCB), 11 October 2011, pp. 1–7. IEEE (2011)
Song, L., Ma, H.: Face liveliness detection based on texture and color features. In: 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 12 April 2019, pp. 418–422. IEEE (2019)
Luan, X., Wang, H., Ou, W., Liu, L.: Face liveness detection with recaptured feature extraction. In: 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), 15 December 2017, pp. 429–432. IEEE (2017)
Angadi, S.A., Kagawade, V.C.: Detection of face spoofing using multiple texture descriptors. In: 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), 21 December 2018, pp. 151–156. IEEE (2018)
Şengür, A., Akhtar, Z., Akbulut, Y., Ekici, S., Budak, Ü.: Deep feature extraction for face liveness detection. In: 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 28 September 2018, pp. 1–4. IEEE (2018)
Vanitha, A., Vaidehi, V., Vasuhi, S.: Liveliness detection in real time videos using color based chromatic moment feature. In: 2018 International Conference on Recent Trends in Advance Computing (ICRTAC), 10 September 2018, pp. 162–167. IEEE (2018)
Sthevanie, F., Ramadhani, K.N.: Spoofing detection on facial images recognition using LBP and GLCM combination. J. Phys. Conf. Series 971(1) 012014 (2018). IOP Publishing
Boulkenafet, Z., Komulainen, J., Hadid, A.: Face anti-spoofing based on color texture analysis. In: 2015 IEEE International Conference on Image Processing (ICIP), 27 September 2015, pp. 2636–2640. IEEE (2015)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Analysis and Machine Intelligence. 24(7), 971–987, 7 August 2002
Zhang, X., Zou, J., He, K., Sun, J.: Accelerating very deep convolutional networks for classification and detection. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 1943–55 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016, pp. 770–778 (2016)
Chingovska, I., Dos Anjos, A.R., Marcel, S.: Biometrics evaluation under spoofing attacks. IEEE Trans. Inf. Forensics Secur. 9(12), 2264–2276 (2014)
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Abdullakutty, F., Elyan, E., Johnston, P. (2021). Face Spoof Detection: An Experimental Framework. In: Iliadis, L., Macintyre, J., Jayne, C., Pimenidis, E. (eds) Proceedings of the 22nd Engineering Applications of Neural Networks Conference. EANN 2021. Proceedings of the International Neural Networks Society, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-80568-5_25
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DOI: https://doi.org/10.1007/978-3-030-80568-5_25
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