Facelog: Login System with User Authentication Toolkit Utilizing Convolutional Neural Network Algorithm

The study introduces FaceLog, a two-layer authentication framework developed to add digital security by incorporating biometric authentication and multi-factor authentication (MFA). The first security layer utilizes a Convolutional Neural Network (CNN)–based facial recognition model with liveness detection to verify user authenticity in real time. Using the Eye Aspect Ratio (EAR) method, the system detects natural eye blinks to distinguish live users from spoofing attempts involving static or digital images. Once facial verification is successful, the system proceeds to second layer of protection, either a One-Time Password (OTP) or a Time-Based One-Time Password (TOTP) for identity confirmation. This structure ensures that even if one authentication factor is compromised, unauthorized access remains effectively prevented. Evaluation results demonstrate high accuracy, precision, recall, and F1-score, supported by excellent ratings in functionality, usability, and compatibility based the criterion of ISO/IEC 25010 software quality model. The findings affirm that combining biometric authentication with multi-factor verification provides a robust, efficient, and user-centered approach to secure modern login systems, addressing the growing challenges of cybersecurity in digital platforms.

Facial Recognition, Convolutional Neural Network (CNN), Liveness Detection

1. Soriano, C. R., Tintiangko, J., & Panaligan, J. H. (2025). Social infrastructures of mobility: Relational encounters between foreign and local digital nomads in the Philippines. City Culture and Society, 43, 100667. https://doi.org/10.1016/j.ccs.2025.100667 [Google Scholar] [Crossref]

2. Albshaier, L., Almarri, S., & Hafizur Rahman, M. M. (2024). A review of blockchain’s role in E-Commerce transactions: Open challenges, and future research directions. Computers, 13(1), 27. https://doi.org/10.3390/computers13010027 [Google Scholar] [Crossref]

3. Khando, K., Islam, M. S., & Gao, S. (2022). The emerging technologies of digital payments and associated challenges: A systematic literature review. Future Internet, 15(1), 21. https://doi.org/10.3390/fi15010021 [Google Scholar] [Crossref]

4. Patra, G. K., Rajaram, S. K., Boddapati, V. N., Kuraku, C., & Gollangi, H. K. (2022). Advancing digital payment systems: Combining AI, big data, and biometric authentication for enhanced security. International Journal of Engineering and Computer Science, 11(08), 10–18535. http://dx.doi.org/10.18535/ijecs/v11i08.4698 [Google Scholar] [Crossref]

5. Salim, M. M., Camacho, D., & Park, J. H. (2024). Digital Twin and federated learning enabled cyberthreat detection system for IoT networks. Future Generation Computer Systems, 161, 701–713. https://doi.org/10.1016/j.future.2024.07.017 [Google Scholar] [Crossref]

6. Varshney, G., Raj, A., Sangwan, D., Abuadbba, S., Mishra, R., & Gao, Y. (2025). A login page transparency and visual similarity-based zero-day phishing defense protocol. Computers & Security, 158, 104598. https://doi.org/10.1016/j.cose.2025.104598 [Google Scholar] [Crossref]

7. Sturman, D., Bell, E. A., Auton, J. C., Breakey, G. R., & Wiggins, M. W. (2024). The roles of phishing knowledge, cue utilization, and decision styles in phishing email detection. Applied Ergonomics, 119, 104309. https://doi.org/10.1016/j.apergo.2024.104309 [Google Scholar] [Crossref]

8. Ha, G., Jia, C., Ge, X., Yuan, J., Chen, H., & Li, M. (2023). Efficient and anonymous password-hardened encryption services. Information Sciences, 653, 119771. https://doi.org/10.1016/j.ins.2023.119771 [Google Scholar] [Crossref]

9. Holthouse, R., Owens, S., & Bhunia, S. (2025). The 23andMe data breach: Analyzing credential stuffing attacks, security vulnerabilities, and mitigation strategies. arXiv preprint arXiv:2502.04303. https://doi.org/10.48550/arXiv.2502.04303 [Google Scholar] [Crossref]

10. Chakraborty, S., Jackson, C., Frazier, M., & Clark, K. (2024, March). A study on password protection and encryption in the era of cyber attacks. In SoutheastCon 2024 (pp. 460–465). IEEE. https://doi.org/10.1109/SoutheastCon52093.2024.10500214 [Google Scholar] [Crossref]

11. Mahdad, A. T., Jubur, M., & Saxena, N. (2024, December). Breaching security keys without root: Fido2 deception attacks via overlays exploiting limited display authenticators. In Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security (pp. 1686–1700). ACM. https://doi.org/10.1145/3658644.3690286 [Google Scholar] [Crossref]

12. Ismail, M., Madathil, N. T., Alalawi, M., Alrabaee, S., Bataineh, M. A., Melhem, S., & Mouheb, D. (2024). Cybersecurity activities for education and curriculum design: A survey. Computers in Human Behavior Reports, 16, 100501. https://doi.org/10.1016/j.chbr.2024.100501 [Google Scholar] [Crossref]

13. Khan, K., Khurshid, A., & Cifuentes-Faura, J. (2024). Is artificial intelligence a new battleground for cybersecurity? Internet of Things, 28, 101428. https://doi.org/10.1016/j.iot.2024.101428 [Google Scholar] [Crossref]

14. Maraveas, C., Rajarajan, M., Arvanitis, K. D., & Vatsanidou, A. (2024). Cybersecurity threats and mitigation measures in agriculture 4.0 and 5.0. Smart Agricultural Technology, 9, 100616. https://doi.org/10.1016/j.atech.2024.100616 [Google Scholar] [Crossref]

15. Amirgaliyev, B., Mussabek, M., Rakhimzhanova, T., & Zhumadillayeva, A. (2025). A review of machine learning and deep learning methods for person detection, tracking and identification, and face recognition with applications. Sensors, 25(5), 1410. https://doi.org/10.3390/s25051410 [Google Scholar] [Crossref]

16. Gill, A., Jain, D., Sharma, J., Kumar, A., & Garg, P. (2024, May). Deep learning approach for facial identification for online transactions. In 2024 International Conference on Emerging Innovations and Advanced Computing (INNOCOMP) (pp. 715–722). IEEE. https://doi.org/10.1109/INNOCOMP63224.2024.00123 [Google Scholar] [Crossref]

17. Dhakal, S., Parvez, A., Kathuria, S., & Chanti, Y. (2024, May). Face recognition technology powered by artificial intelligence for enhanced security measures. In 2024 Parul International Conference on Engineering and Technology (PICET) (pp. 1–6). IEEE. https://doi.org/10.1109/PICET60765.2024.10716186 [Google Scholar] [Crossref]

18. Nosrati, L., Bidgoli, A. M., & Javadi, H. H. S. (2024). Identifying people’s faces in smart banking systems using artificial neural networks. International Journal of Computational Intelligence Systems, 17(1), 9. https://doi.org/10.1007/s44196-023-00383-7 [Google Scholar] [Crossref]

19. Malempati, M. (2024). Generative AI-driven innovation in digital identity verification: Leveraging neural networks for next-generation financial security. SSRN. https://doi.org/10.2139/ssrn.5204991 [Google Scholar] [Crossref]

20. Shukla, S., Varshney, G., Singh, S., & Goel, S. (2025). A passwordless MFA utilizing biometrics, proximity, and contactless communication. Information Security Journal: A Global Perspective, 1–22. https://doi.org/10.1080/19393555.2025.2536033 [Google Scholar] [Crossref]

21. Erdogmus, N., & Marcel, S. (2014). Spoofing face recognition with 3D masks. IEEE Transactions on Information Forensics and Security, 9(7), 1084–1097. https://doi.org/10.1109/TIFS.2014.2322255 [Google Scholar] [Crossref]

22. George, A., Mostaani, Z., Geissenbuhler, D., Nikisins, O., Anjos, A., & Marcel, S. (2019). Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Transactions on Information Forensics and Security, 15, 42–55. https://doi.org/10.1109/TIFS.2019.2916652 [Google Scholar] [Crossref]

23. Rehman, Y. A. U., Po, L. M., Liu, M., Zou, Z., Ou, W., & Zhao, Y. (2019). Face liveness detection using convolutional-features fusion of real and deep network generated face images. Journal of Visual Communication and Image Representation, 59, 574–582. https://doi.org/10.1016/j.jvcir.2019.02.014 [Google Scholar] [Crossref]

24. Yu, C., Yao, C., Pei, M., & Jia, Y. (2019). Diffusion-based kernel matrix model for face liveness detection. Image and Vision Computing, 89, 88–94. https://doi.org/10.1016/j.imavis.2019.06.009 [Google Scholar] [Crossref]

25. Al-Mutairi, Abdulrahman, & Al-Sahli, Rayan. (2024). Secure Authentication System based on Multi-Factor Authentication. https://doi.org/10.13140/RG.2.2.24880.74247 [Google Scholar] [Crossref]

26. Liu, J., Qin, H., Wu, Y., & Liang, D. (2022). AnchorFace: Boosting TAR@FAR for practical face recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, 36(2), 1371–1379. https://doi.org/10.1609/aaai.v36i2.20063 [Google Scholar] [Crossref]

27. Reichinger, D., Lechner, U., & Pichler, A. (2021). Continuous mobile user authentication using combined behavioral and physiological biometrics. Applied Sciences, 11(24), 11756. https://doi.org/10.3390/app112411756 [Google Scholar] [Crossref]

28. Vukadinović, V., Milosević, B., et al. (2023). Biometric systems for identification and verification scenarios using threshold-based matching and performance trade-offs. Neural Computing & Applications. https://doi.org/10.1007/s00521-023-09390-3 [Google Scholar] [Crossref]

29. Yang, W., Wang, S., Hu, J., Zheng, G., & Valli, C. (2019). Security and Accuracy of Fingerprint-Based Biometrics: A Review. Symmetry, 11(2), 141. https://doi.org/10.3390/sym11020141 [Google Scholar] [Crossref]

30. Iskandar, A., et al. (2024). Biometric systems for identification and verification scenarios using threshold-based matching and performance trade-offs. Neural Computing & Applications. https://doi.org/10.1007/s00521-023-09390-3 [Google Scholar] [Crossref]

31. Yang, W., et al. (2021). Biometrics for Internet-of-Things Security: A Review. Sensors (special issue). https://doi.org/10.3390/s21082728 [Google Scholar] [Crossref]

32. FIDO Alliance. (2023). FIDO Biometric Certification Requirements: Performance Criteria for FAR and FRR. FIDO Alliance.https://fidoalliance.org/specs/biometric/requirements/Biometrics-Requirements-v3.0-fd-20230111.html [Google Scholar] [Crossref]

33. National Institute of Standards and Technology (NIST). (2022). Guidelines for Biometric Performance and Error Rate Management. U.S. Department of Commerce. [Google Scholar] [Crossref]

34. Abdelfatah, R. I. (2024). Robust biometric identity authentication scheme using quantum voice encryption and quantum secure direct communications for cybersecurity. Journal of King Saud University - Computer and Information Sciences, 36(5), 102062. https://doi.org/10.1016/j.jksuci.2024.102062 [Google Scholar] [Crossref]

35. Shinde, S. R., Bongale, A. M., Dharrao, D., & Thepade, S. D. (2025). An enhanced light weight face liveness detection method using deep convolutional neural network. MethodsX, 14, 103229. https://doi.org/10.1016/j.mex.2025.103229 [Google Scholar] [Crossref]

36. Prasad, P., Lakshmi, A., Kautish, S., Singh, S., Shrivastava, R., Almazyad, A., ... & Ali, M. (2024). Robust Facial Biometric Authentication System Using Pupillary Light Reflex for Liveness Detection of Facial Images. Computer Modeling in Engineering & Sciences, 139(1), 725. https://doi.org/10.32604/cmes.2023.030640 [Google Scholar] [Crossref]

37. Khairnar, S., Gite, S., Pradhan, B., Thepade, S., & Alamri, A. (2025). Optimizing CNN architectures for face liveness detection: performance, efficiency, and generalization across datasets. Computer Modeling in Engineering & Sciences, 143(3), 3677. https://doi.org/10.32604/cmes.2025.058855 [Google Scholar] [Crossref]

38. Hossain, M. I. (2023). Software development life cycle (SDLC) methodologies for information systems project management. International Journal for Multidisciplinary Research, 5(5), 1–36. E-ISSN: 2582-2160 [Google Scholar] [Crossref]

39. Mahmood, M., Tasnim, N., Saimu, T. M., Rahman, M. A., Quadir, H. S., Rashed, M. G., & Das, D. (2025, February). A novel approach to liveness detection: Real-time eye blink, head movement, and lip movement analysis. In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE) (pp. 1–6). IEEE. https://doi.org/10.1109/ECCE64574.2025.11014007 [Google Scholar] [Crossref]

40. Sheng, H., & Lau, M. (2024). Optimising real-time facial expression recognition with ResNet architectures. Journal of Machine Intelligence and Data Science (JMIDS), 5(1), 33–45. https://doi.org/10.11159/jmids.2024.005 [Google Scholar] [Crossref]

• Analyzing Retail Sales Trends to Uncover Consumer Buying Patterns and Support Evidence-Based Decision-Making for Producers