Enhancing User Authentication with Facial Recognition and Feature-Based Credentials

Yasmin Makki Mohialden; Nadia Mahmood Hussien; Doaa Muhsin Abd Ali

doi:10.37899/journallamultiapp.v4i6.903

Yasmin Makki Mohialden Computer Science Department, Collage of Science, Mustansiriyah University, Iraq
Nadia Mahmood Hussien Computer Science Department, Collage of Science, Mustansiriyah University, Iraq
Doaa Muhsin Abd Ali Department of Computer Science, College of Education, Mustansiriyah University, Iraq

DOI: https://doi.org/10.37899/journallamultiapp.v4i6.903

Keywords: Face Recognition System, Facial Feature Extraction, User Credentials, Attribute Hashing, Password Generation, Biometric Authentication, Unique Password Generation

Abstract

This research proposes a novel and trustworthy user authentication method that creates individualized and trusted credentials based on distinctive facial traits using facial recognition technology. The ability to easily validate user identification across various login methods is provided by this feature. The fundamental elements of this system are face recognition, feature extraction, and the hashing of characteristics to produce usernames and passwords. This method makes use of the OpenCV library, which is free software for computer vision. Additionally, it employs Hashlib for secure hashing and Image-based Deep Learning for Identification (IDLI) technology to extract facial tags. For increased security and dependability, the system mandates a maximum of ten characters for users and passwords. By imposing this restriction, the system increases its resilience by reducing any possible weaknesses in its defense. The policy also generates certificates that are neatly arranged in an Excel file for easy access and management. To improve user data and provide reliable biometric authentication, this study intends to create and implement a recognition system that incorporates cutting-edge approaches such as face feature extraction, feature hashing, and password creation. Additionally, the system has robust security features using face recognition.

References

Gupta, R., Almuzaini, K., Pateriya, R., Shah, K., Shukla, P., & Akwafo, R. (2022). An Improved Secure Key Generation Using Enhanced Identity-Based Encryption for Cloud Computing in Large-Scale 5G. Wireless Communications and Mobile Computing. https://doi.org/10.1155/2022/7291250.

Kim, S., & Ro, Y. (2019). Attended Relation Feature Representation of Facial Dynamics for Facial Authentication. IEEE Transactions on Information Forensics and Security, 14, 1768-1778. https://doi.org/10.1109/TIFS.2018.2885276.

Mulani, A. O., & Shinde, G. N. (2021). An approach for robust digital image watermarking using DWT‐PCA. Journal of Science and Technology, 6(1).

Olanrewaju, R. F., Khan, B. U. I., Morshidi, M. A., Anwar, F., & Kiah, M. L. B. M. (2021). A frictionless and secure user authentication in web-based premium applications. IEEE Access, 9, 129240-129255.

Papaspirou, V., Papathanasaki, M., Maglaras, L., Kantzavelou, I., Douligeris, C., Ferrag, M., & Janicke, H. (2022). Security Revisited: Honeytokens meet Google Authenticator. 2022 7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), 1-8. https://doi.org/10.1109/SEEDA-CECNSM57760.2022.9932907.

Spooren, J., Preuveneers, D., & Joosen, W. (2019). PPG2Live: Using dual PPG for active authentication and liveness detection. 2019 International Conference on Biometrics (ICB), 1-6. https://doi.org/10.1109/ICB45273.2019.8987330.

Vangala, A., Sutrala, A., Das, A., & Jo, M. (2021). Smart Contract-Based Blockchain-Envisioned Authentication Scheme for Smart Farming. IEEE Internet of Things Journal, 8, 10792-10806. https://doi.org/10.1109/JIOT.2021.3050676.

Yu, S., Park, K., Park, Y., Kim, H., & Park, Y. (2020). A lightweight three-factor authentication protocol for digital rights management system. Peer-to-Peer Networking and Applications, 1-17. https://doi.org/10.1007/s12083-019-00836-x.