FACE DETECTION AND ANTI-SPOOFING ON DESKTOP APPLICATIONS USING YOU ONLY LOOK ONCE
DOI:
https://doi.org/10.47080/6qntes73Keywords:
Computer Vision, Face Recognition, Liveness Detection, Real-time Detection, YOLOv8Abstract
In the digital era, facial recognition systems have become increasingly vulnerable to spoofing attacks, as demonstrated by cases of identity theft using photos or smartphone screens. This study develops a real-time face liveness detection system using YOLOv8 to address these vulnerabilities. Under controlled laboratory conditions, the system achieved exceptional performance metrics: accuracy of 1.0, precision of 1.0, and recall of 1.0, with a mean Average Precision (mAP) of 0.96. However, this study reveals critical insights about the challenges of real-world deployment, including significant performance degradation under poor lighting conditions where genuine faces were misclassified as spoofed images. Compared to existing methods such as Attention-Based Two-Stream CNN (accuracy: 0.91) and Deep Spatial Gradient approaches (accuracy: 0.90-0.92), our system demonstrates superior performance in controlled environments but highlights the persistent challenge of environmental variability in practical applications. These findings emphasize the need for robust preprocessing techniques and diverse training datasets to bridge the gap between laboratory performance and real-world reliability. The study contributes to understanding the limitations of current face anti-spoofing technologies and provides a foundation for developing more robust systems suitable for practical deployment.
References
Abbas, Y., Rehman, U., Po, L. M., & Liu, M. (2017). Deep Learning for Face Anti-Spoofing: An End-to-End Approach.
Al-Huda Taha, N., Hassan, T. M., & Younis, M. A. (2021). Face Spoofing Detection Using Deep CNN. In Turkish Journal of Computer and Mathematics Education (Vol. 12, Issue 13).
Chen, H., Hu, G., Lei, Z., Chen, Y., Robertson, N. M., & Li, S. Z. (2019). Attention-Based Two-Stream Convolutional Networks for Face Spoofing Detection.
Deb, D., & Jain, A. K. (2021). Look Locally Infer Globally: A Generalizable Face Anti-Spoofing Approach. IEEE Transactions on Information Forensics and Security, 16, 1143–1157. https://doi.org/10.1109/TIFS.2020.3029879
Ebihara, A. F., Sakurai, K., & Imaoka, H. (2021). Efficient Face Spoofing Detection with Flash. IEEE Transactions on Biometrics, Behavior, and Identity Science, 3(4), 535–549. https://doi.org/10.1109/TBIOM.2021.3076816
George, A., & Marcel, S. (2020). Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks. http://arxiv.org/abs/2007.11457
Hadiprakoso, R. B. (2020). Face anti-spoofing method with blinking eye and HSV texture analysis. IOP Conference Series: Materials Science and Engineering, 1007(1). https://doi.org/10.1088/1757-899X/1007/1/012034
Jia, S., Guo, G., & Xu, Z. (2020). A survey on 3D mask presentation attack detection and countermeasures. Pattern Recognition, 98. https://doi.org/10.1016/j.patcog.2019.107032
Khairnar, S., Gite, S., Kotecha, K., & Thepade, S. D. (2023). Face Liveness Detection Using Artificial Intelligence Techniques: A Systematic Literature Review and Future Directions. In Big Data and Cognitive Computing (Vol. 7, Issue 1). MDPI. https://doi.org/10.3390/bdcc7010037
Koshy, R., & Mahmood, A. (2020). Enhanced deep learning architectures for face liveness detection for static and video sequences. Entropy, 22(10), 1–27. https://doi.org/10.3390/e22101186
Mohamed, A. A., Nagah, M. M., Abdelmonem, M. G., Ahmed, M. Y., El-Sahhar, M., & Ismail, F. H. (2021). Face Liveness Detection Using a sequential CNN technique. 2021 IEEE 11th Annual Computing and Communication Workshop and Conference, CCWC 2021, 1483–1488. https://doi.org/10.1109/CCWC51732.2021.9376030
Pérez-Cabo, D., Jiménez, D., Costa-Pazo Gradiant, A., acosta, S., & Roberto López-Sastre, gradiantorg J. (2020). Deep Anomaly Detection for Generalized Face Anti-Spoofing. https://www.samsung.com/my/support/mobile-devices/
Phoo Pyae Pyae Linn, E. C. H. (2021). Face Anti-spoofing using Eyes Movement and CNN-based Liveness Detection - Phoo Pyae Pyae Linn, Ei Chaw Htoon.
Purnapatra, S., Smalt, N., Bahmani, K., Das, P., Yambay, D., Mohammadi, A., George, A., Bourlai, T., Marcel, S., Schuckers, S., Fang, M., Damer, N., Boutros, F., Kuijper, A., Kantarci, A., Demir, B. B., Yildiz, Z., Ghafoory, Z., Dertli, H., … Ramachandra, R. (2021). Face Liveness Detection Competition (LivDet-Face)-2021. https://face2021.livdet.org/
Sudeep Thepade, Prasad Jagdale, Amit Bhingurde, & Shwetali Erandole. (2020). 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) : February 2-5, Doha, Qatar. IEEE.
Surantha, N., & Sugijakko, B. (2024). Lightweight face recognition-based portable attendance system with liveness detection. Internet of Things (Netherlands), 25. https://doi.org/10.1016/j.iot.2024.101089
Surden, H. (2014). Machine Learning and Law Machine Learning and Law Citation Information Citation Information Copyright Statement. https://scholar.law.colorado.edu/faculty-articles/81.
Wang, Z., Yu, Z., Zhao, C., Zhu, X., Qin, Y., Zhou, Q., Zhou, F., & Lei, Z. (2020). Deep spatial gradient and temporal depth learning for face anti-spoofing. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 5041–5050. https://doi.org/10.1109/CVPR42600.2020.00509
Wei, Y., Machica, I. K. D., Dumdumaya, C. E., Arroyo, J. C. T., & Delima, A. J. P. (2022). Liveness Detection Based on Improved Convolutional Neural Network for Face Recognition Security. International Journal of Emerging Technology and Advanced Engineering, 12(8), 45–53. https://doi.org/10.46338/ijetae0822_06
Xu, W., Liu, J., Zhang, S., Zheng, Y., Lin, F., Han, J., Xiao, F., & Ren, K. (2021). RFace: Anti-Spoofing Facial Authentication Using COTS RFID.
Xu, X., Xiong, Y., & Xia, W. (2022). On Improving Temporal Consistency for Online Face Liveness Detection System.
Yang Chen, Tian Wang, Jingjing Wang, Peng Shi, Guangcun Shan, & Hichem Snoussi. (2019). Proceedings, 2019 Chinese Automation Congress (CAC2019) : Nov. 22-24, 2019, Hangzhou, China. IEEE.
Yang, X., Luo, W., Bao, L., Gao, Y., Gong, Di., Zheng, S., Li, Z., & Liu, W. (2019). Face anti-spoofing: Model matters, so does data. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 3502–3511. https://doi.org/10.1109/CVPR.2019.00362
Zhang, Y.-J., Chen, J.-Y., & Lu, Z.-M. (2022). Face anti-spoofing detection based on color texture structure analysis. Taiwan Ubiquitous Information, 7(2). Kompas.com. (2023, January 30). Data diri dipalsukan, nama Renaldy Bosito terjerat kredit mobil hingga Rp1 miliar. Retrieved from https://megapolitan.kompas.com/read/2023/01/30/18275691/data-diri-dipalsukan-nama-renaldy-bosito-terjerat-kredit-mobil-hingga.
