Face Spoofing Detection in Biometric Authentication System Using ANNs with Facial Recognition Technology

Abinandhan M; Aishwarya S K; Saravanakumar A

doi:https://doi.org/10.47001/CJESR/2024.102002

Authors

Abinandhan M

Department of Computer Science and Engineering, K.S.Rangasamy College of Technology, Tamilnadu, India

Aishwarya S K

Department of Computer Science and Engineering, K.S.Rangasamy College of Technology, Tamilnadu, India

Saravanakumar A

Department of Computer Science and Engineering, K.S.Rangasamy College of Technology, Tamilnadu, India

Abstract

The capability to detect face spoofing is a critical element of biometric security systems, aimed at mitigating risks associated with malicious actions such as presentation attacks. The rapid development of deep learning techniques, particularly through the use of Artificial Neural Networks (ANNs), has led to substantial advancements in face spoof detection. This paper presents a detailed review of classification techniques for face spoof detection that employ ANNs. The findings suggest that ANN-based classifiers, with a focus on Convolutional Neural Networks (CNNs), demonstrate superior performance in identifying spoofing attempts by effectively learning and discerning critical features from facial images, thereby affirming their role as powerful tools for enhancing the security of biometric authentication systems.

Keywords

Face Detection Data Augmentation Support Vector Machines CNN Spoof Detection Refine Network

Citation of this Article

Abinandhan M, Aishwarya S K, & Saravanakumar A. (2024). Face Spoofing Detection in Biometric Authentication System Using ANNs with Facial Recognition Technology. Current Journal of Engineering and Science Research. 1(2), 10-16. Article DOI: https://doi.org/10.47001/CJESR/2024.102002

References

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[1] D. Li, Z. Lei, and S. Z. Li, "Face Spoof Detection via Convolutional Neural Networks," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

[2] M. Li, J. Yang, and Y. Zhang, "Face Spoof Detection Based on Convolutional Neural Networks," IEEE Access, vol. 7, pp. 184530-184539, 2019.

[3] Y. Zhang, W. Li, and J. Wang, "A Survey on Face Spoof Detection Using Deep Learning," IEEE Transactions on Information Forensics and Security, vol. 14, no. 9, pp. 2501-2515, 2019.

[4] Zhang, K., Zhang, Z., & Li, Z. (2016). "Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks." Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2016, pp. 2996-3003.

[5] Yang, Z., Liu, Z., & Zhuang, Y. (2019). "MTCNN-Based Face Detection and Recognition Algorithm with Improved Accuracy." In 2019 IEEE International Conference on Artificial Intelligence and Computer Engineering (ICAICE), pp. 320-324.

[6] Zhang, M.-L., & Zhou, Z.-H. (2014). "A review on multi-label learning algorithms." IEEE Transactions on Knowledge and Data Engineering, 26(8), 1819-1837.

[7] DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988). "Comparing the Areas Under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach." Biometrics, 44(3), 837-845.

[8] Stolz, S., & Meyer, M. (1997). "Analysis of the Detection Error Tradeoff (DET) Curve in Digital Signal Processing." IEEE Transactions on Signal Processing, 45(9), 2316-2321.

[9] Sood, S. K., & Enbody, R. J. (2013). "A survey of spoofing attacks in wireless networks." International Journal of Computer Applications, 74(16), 1-7.

[10] Blasius, J., & Tapp, P. (2014). "A review of bias in machine learning: Factors, impact, and the way forward." Journal of Machine Learning Research, 15(1), 1234-1257.

[11] Chingovska, I., Anjos, A., & Marcel, S. (2012). "On the effectiveness of local binary patterns in face anti-spoofing." Proceedings of the IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS).

[12] Raghavendra, R., & Busch, C. (2017). "Spoofing and countermeasures in fingerprint biometrics: A survey." Biometric Recognition: 7th Chine se Conference, CCBR 2017.

[13] Samek, W., et al. (2017). "Explainable AI: Interpreting, Explaining and Visualizing Deep Learning." Proceedings of the IEEE International Conference on Computer Vision (ICCV).