DEEP LEARNING DETECTION OF FACIAL BIOMETRIC PRESENTATION ATTACK

Received: 11th May 2022; Revised: 19th June 2022, 01st July 2022, 08th July 2022; Accepted: 08th July 2022

Authors

  • Ahmed Muthanna Shibel Department of Computer and Communication Systems Engineering, Faculty of Engineering, University Putra Malaysia (UPM), Malaysia
  • Sharifah Mumtazah Syed Ahmad Department of Computer and Communication Systems Engineering, Faculty of Engineering, University Putra Malaysia (UPM), Malaysia
  • Luqman Hakim Musa Department of Computer and Communication Systems Engineering, Faculty of Engineering, University Putra Malaysia (UPM), Malaysia
  • Mohammed Nawfal Yahya Department of Computer and Communication Systems Engineering, Faculty of Engineering, University Putra Malaysia (UPM), Malaysia

DOI:

https://doi.org/10.20319/lijhls.2022.82.0118

Keywords:

Biometric, Deep Learning, Presentation Attack, Face Liveness Detection, CNN

Abstract

Face recognition systems have gained increasing importance in today’s society, which applications range from access controls to secure systems to electronic devices such as mobile phones and laptops. However, the security of face recognition systems is currently being threatened by the emergence of spoofing attacks that happens when someone tries to unauthorizedly bypass the biometric system by presenting a photo, 3-dimensional mask, or replay video of a legit user. The video attacks are perhaps one of the most frequent, cheapest, and simplest spoofing techniques to cheat face recognition systems. This research paper focuses on face liveness detection in video attacks, intending to determine if the provided input biometric samples came from a live face or spoof attack by extracting frames from the videos and classifying them by using the Resnet-50 deep learning algorithm. The majority voting mechanism is used as a decision fusion to derive a final verdict. The experiment was conducted on the spoof videos of the Replay-attack dataset. The results demonstrated that the optimal number of frames for video liveness detection is 3 with an accuracy of 96.93 %.  This result is encouraging since the low number of frames requires minimal time for processing.

References

Jain, A. K., Ross, A., & Prabhakar, S. (2004). An introduction to biometric recognition. IEEE Transactions on circuits and systems for video technology, 14(1), 4-20. https://doi.org/10.1109/TCSVT.2003.818349

Bhatia, R. (2013). Biometrics and face recognition techniques. International Journal of Advanced Research in Computer Science and Software Engineering, 3(5), 93-99.

Tripathi, K. P. (2011). A comparative study of biometric technologies with reference to human interface. International Journal of Computer Applications, 14(5), 10-15. https://doi.org/10.5120/1842-2493

Hassaballah, M., & Aly, S. (2015). Face recognition: challenges, achievements and future directions. IET Computer Vision, 9(4), 614-626. https://doi.org/10.1049/iet-cvi.2014.0084

Ghiass, R. S., Arandjelović, O., Bendada, A., & Maldague, X. (2014). Infrared face recognition: A comprehensive review of methodologies and databases. Pattern Recognition, 47(9), 2807-2824. https://doi.org/10.1016/j.patcog.2014.03.015

Galbally, J., Marcel, S., & Fierrez, J. (2014). Biometric antispoofing methods: A survey in face recognition. IEEE Access, 2, 1530-1552. https://doi.org/10.1109/ACCESS.2014.2381273

Pinto, A., Schwartz, W. R., Pedrini, H., & de Rezende Rocha, A. (2015). Using visual rhythms for detecting video-based facial spoof attacks. IEEE Transactions on Information Forensics and Security, 10(5), 1025-1038. https://doi.org/10.1109/TIFS.2015.2395139

Bagga, M., & Singh, B. (2016, March). Spoofing detection in face recognition: A review. In 2016 3rd International Conference on Computing for Sustainable Global Development (INDIA Com) (pp. 2037-2042). IEEE.

Chakka, M. M., Anjos, A., Marcel, S., Tronci, R., Muntoni, D., Fadda, G., & Pietikäinen, M. (2011, October). Competition on counter measures to 2-d facial spoofing attacks. In 2011 International Joint Conference on Biometrics (IJCB) (pp. 1-6). IEEE https://doi.org/10.1109/IJCB.2011.6117509

Alotaibi, A., & Mahmood, A. (2016, June). Enhancing computer vision to detect face spoofing attack utilizing a single frame from a replay video attack using deep learning. In 2016 International Conference on Optoelectronics and Image Processing (ICOIP) (pp. 1-5). IEEE. https://doi.org/10.1109/OPTIP.2016.7528488

Koshy, R., & Mahmood, A. (2020). Enhanced Deep Learning Architectures for Face Liveness Detection for Static and Video Sequences. Entropy, 22 (10), 1186. https://doi.org/10.3390/e22101186

Sabaghi, A., Oghbaie, M., Hashemifard, K., & Akbari, M. (2021). Deep Learning meets Liveness Detection: Recent Advancements and Challenges. arXiv preprint arXiv:2112.14796.

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). https://doi.org/10.1109/CVPR.2016.90

Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003). Face recognition: A literature survey. ACM computing surveys (CSUR), 35(4), 399-458. https://doi.org/10.1145/954339.954342

Phillips, P. J., Flynn, P. J., Scruggs, T., Bowyer, K. W., Chang, J., Hoffman, K., ... & Worek, W. (2005, June). Overview of the face recognition grand challenge. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05) (Vol. 1, pp. 947-954). IEEE.

Chingovska, I., Erdogmus, N., Anjos, A., & Marcel, S. (2016). Face recognition systems under spoofing attacks. In Face Recognition Across the Imaging Spectrum (pp. 165-194). Springer, Cham. https://doi.org/10.1007/978-3-319-28501-6_8

Bashar, A. (2019). Survey on evolving deep learning neural network architectures. Journal of Artificial Intelligence, 1(02), 73-82. https://doi.org/10.36548/jaicn.2019.2.003

Liu, H., & Lang, B. (2019). Machine learning and deep learning methods for intrusion detection systems: A survey. applied sciences, 9(20), 4396. https://doi.org/10.3390/app9204396

Thrall, J. H., Li, X., Li, Q., Cruz, C., Do, S., Dreyer, K., & Brink, J. (2018). Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. Journal of the American College of Radiology, 15(3), 504-508. https://doi.org/10.1016/j.jacr.2017.12.026

Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of big data, 2(1), 1-21. https://doi.org/10.1186/s40537-014-0007-7

Lokhande, B. P., & Gharde, S. S. (2015). A Review on Large-scale Video Classification with Recurrent Neural Network (RNN). International Journal of Computer Science and Information Technologies, Jalgaon, India.

Chingovska, I., Anjos, A., & Marcel, S. (2012, September). On the effectiveness of local binary patterns in face anti-spoofing. In 2012 BIOSIG-proceedings of the international conference of biometrics special interest group (BIOSIG) (pp. 1-7). IEEE.

Carneiro, T., Da Nóbrega, R. V. M., Nepomuceno, T., Bian, G. B., De Albuquerque, V. H. C., & Reboucas Filho, P. P. (2018). Performance analysis of google colaboratory as a tool for accelerating deep learning applications. IEEE Access, 6, 61677-61685. https://doi.org/10.1109/ACCESS.2018.2874767

Hashemi, M. (2019). Enlarging smaller images before inputting into convolutional neural network: zero-padding vs. interpolation. Journal of Big Data, 6(1), 1-13. https://doi.org/10.1186/s40537-019-0263-7

Hashemi, M. (2020). Web page classification: a survey of perspectives, gaps, and future directions. Multimedia Tools and Applications, 79(17), 11921-11945. https://doi.org/10.1007/s11042-019-08373-8

Brownlee, J. (2018). What is the Difference Between a Batch and an Epoch in a Neural Network. Machine Learning Mastery, 20.

Carney, J. G., & Cunningham, P. (1998). The epoch interpretation of learning. IEEE Transaction on Neural Networks, 8, 111-116

Downloads

Published

2022-07-15

How to Cite

Shibel, A. M., Ahmad, S. M. S., Musa, L. H., & Yahya, M. N. (2022). DEEP LEARNING DETECTION OF FACIAL BIOMETRIC PRESENTATION ATTACK: Received: 11th May 2022; Revised: 19th June 2022, 01st July 2022, 08th July 2022; Accepted: 08th July 2022. LIFE: International Journal of Health and Life-Sciences, 8(2), 01–18. https://doi.org/10.20319/lijhls.2022.82.0118