Face Recognition System For Student Identification Using VGG16 Convolutional Neural Network
Keywords:
AI-driven security, Convolutional Neural Network, Facial Recognition, VGG16 ArchitectureAbstract
In this paper, we present a robust facial recognition system designed to identify students at Department of Informatics in Universitas Malikussaleh using a Convolutional Neural Network (CNN) algorithm, specifically the VGG16 architecture. The advancement of information technology and machine learning has significantly improved facial recognition capabilities, establishing it as a reliable alternative to traditional identification methods such as fingerprinting and iris scanning. Our approach leverages a diverse dataset captured from five different angles, enhancing the representation of facial features and improving model training. The system development comprises several critical stages, including image acquisition, preprocessing, model training with training and validation data, and performance evaluation. Experimental results indicate that the CNN model achieves an impressive accuracy of 99.09% on training data and 100% on both validation and testing datasets. These findings affirm the model's high classification accuracy across the tested classes, underscoring the effectiveness of the VGG16-based CNN in facial recognition applications. The implications of this study suggest that the developed system can significantly enhance digital attendance and security systems, catering to the growing demand for reliable AI-driven security technologies in contemporary society. We anticipate that with its promising outcomes, this system can be implemented on a larger scale, contributing to the ongoing advancement of AI-based security solutions.
References
[1] Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003). Face recognition: A literature survey. ACM computing surveys (CSUR), 35(4), 399-458.
[2] Parkhi, O., Vedaldi, A., & Zisserman, A. (2015). Deep face recognition. In BMVC 2015-Proceedings of the British Machine Vision Conference 2015. British Machine Vision Association.
[3] Ranjini, M. M. D., Jeyaraj, M. P., Kumar, M. S., Prasath, T. A., & Prabhakar, G. (2023, July). Haar Cascade Classifier-based Real-Time Face Recognition and Face Detection. In 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 990-995). IEEE.
[4] Budiman, A., Yaputera, R. A., Achmad, S., & Kurniawan, A. (2023). Student attendance with face recognition (LBPH or CNN): Systematic literature review. Procedia Computer Science, 216, 31-38.
[5] Sharifani, K., & Amini, M. (2023). Machine learning and deep learning: A review of methods and applications. World Information Technology and Engineering Journal, 10(07), 3897-3904.
[6] Liu, F., Chen, D., Wang, F., Li, Z., & Xu, F. (2023). Deep learning based single sample face recognition: a survey. Artificial Intelligence Review, 56(3), 2723-2748.
[7] Lin, Y., Xie, S., Ghose, D., Liu, X., You, J., Korhonen, J., ... & Dash, S. P. (2024). FishIR: Identifying Pufferfish Individual Based on Deep Learning and Face Recognition. IEEE Access.
[8] Autade, A., Adhav, P., BabarPatil, A., Dhumal, A., Vispute, S., Rajeswari, K., ... & Rathi, S. (2023, August). Automated Multi Face Recognition and Identification using Facenet and VGG-16 on Real-World Dataset for Attendance Monitoring System. In 2023 7th International Conference On Computing, Communication, Control And Automation (ICCUBEA) (pp. 1-5). IEEE.
[9] Muhammad Fikry. “Performance Analysis of Smart Technology With Face Detection Using YOLOv3 and InsightFace for Student Attendance Monitoring”. International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 4, June 2024, pp. 3490 -, https://ijisae.org/index.php/IJISAE/article/view/6865.
[10] Boutros, F., Struc, V., Fierrez, J., & Damer, N. (2023). Synthetic data for face recognition: Current state and future prospects. Image and Vision Computing, 135, 104688.
[11] Krichen, M. (2023). Convolutional neural networks: A survey. Computers, 12(8), 151.
[12] Yamsani, N., Jabar, M. B., Adnan, M. M., Hussein, A. H. A., & Chakraborty, S. (2023, December). Facial Emotional Recognition Using Faster Regional Convolutional Neural Network with VGG16 Feature Extraction Model. In 2023 3rd International Conference on Mobile Networks and Wireless Communications (ICMNWC) (pp. 1-6). IEEE.
[13] Riehl, K., Neunteufel, M., & Hemberg, M. (2023). Hierarchical confusion matrix for classification performance evaluation. Journal of the Royal Statistical Society Series C: Applied Statistics, 72(5), 1394-1412.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Chrisnata Manihuruk; Muhammad Fikry, Hafizh Al Kautsar Aidilof
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Copyright Notice
Authors published in this journal agree to the following terms:
1. The copyright of each article is retained by the author (s).
2. The author grants the journal the first publication rights with the work simultaneously licensed under the Creative Commons Attribution License, allowing others to share the work with an acknowledgment of authorship and the initial publication in this journal.
3. Authors may enter into separate additional contractual agreements for the non-exclusive distribution of published journal versions of the work (for example, posting them to institutional repositories or publishing them in a book), with acknowledgment of their initial publication in this journal.
4. Authors are permitted and encouraged to post their work online (For example in the Institutional Repository or on their website) before and during the submission process, as this can lead to productive exchanges, as well as earlier and larger citations of published work.
5. Articles and all related material published are distributed under a Creative Commons Attribution-ShareAlike 4.0 International License.
