Skip to main content
Article | Green Intelligent Systems and Applications | Volume 6 - Issue 1 - 2026 | 161−173 | https://doi.org/10.53623/gisa.v6i1.1197
© 2026 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

Preliminary Study of ResNet-Based Facial Identification for Access Control Systems

Author(s): Andre Kurniawan , Ery Hartati
Author(s) information:
Faculty of Computer Science and Engineering, Universitas Multi Data Palembang, South Sumatra, Indonesia

Corresponding author

Green Intelligent Systems and Applications

Volume 6 - Issue 1 - 2026

 About the journal
Submit your article

Facial recognition is an important application of artificial intelligence (AI) and computer vision in modern security systems, particularly for automated access control. This study aimed to implement and evaluate a Residual Network (ResNet)-based facial identification system for door access control applications. A publicly available facial image dataset was used and was divided into training (70%), validation (20%), and testing (10%) subsets. The proposed methodology consisted of data preprocessing, ResNet-based model training, and performance evaluation using accuracy and loss metrics. The model was trained for 10 epochs to assess its initial learning capability. The experimental results showed relatively low performance, with training accuracy ranging from 3.6% to 3.8% and validation accuracy of approximately 3.6%, while loss values remained high throughout the training process. These findings indicated that the model was unable to effectively learn discriminative facial features from the dataset and exhibited signs of underfitting. The limited performance was likely associated with insufficient dataset diversity, suboptimal preprocessing procedures, and non-optimized training parameters. The study highlighted the challenges of implementing ResNet-based facial recognition systems under constrained training conditions. Future work should focus on expanding the dataset, applying data augmentation techniques, optimizing hyperparameters, and utilizing pretrained models to improve recognition performance and system reliability.

Read Full-Text
Previous article
Next article

He, K.; Zhang, X.; Ren, S.; Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. https://doi.org/10.1109/CVPR.2016.90.

Diba, M.; Khosravi, H. (2024). SNResNet: A New Architecture Based on SqNxt Blocks and Rish Activation for Efficient Face Recognition. Traitement du Signal, 41, 235–244. https://doi.org/10.18280/ts.410235.

Deng, N.; Lin, J. (2025). Residential Access Control Facial Recognition Based on Improved ResNet Computing Model. Traitement du Signal, 42, 295–304. https://doi.org/10.18280/ts.420130.

Satapathy, A.; Livingston, J.L.M. (2023). A Lightweight Convolutional Neural Network Built on Inception-Residual and Reduction Modules for Deep Facial Recognition in Realistic Conditions. Connection Science, 35, 1–21. https://doi.org/10.1080/13682199.2023.2176735.

Xiao, L.; Li, X.; Luo, Y.; et al. (2024). Research on the Application of CNN Face Recognition Technology in the Airport. Advances in Transportation Studies and Development Engineering, 1, 1–8. https://doi.org/10.3233/ATDE240470.

Obaid, A.M.; et al. (2023). Exploring the Potential of A-ResNet in Person-Independent Face Recognition and Classification. International Journal of Advances in Soft Computing and Its Applications, 15, 1–15. https://doi.org/10.2478/ijanmc-2023-0052.

Zhang, M.; Zhang, Y.; Zhang, Q. (2023). Attention-Mechanism-Based Models for Unconstrained Face Recognition with Mask Occlusion. Electronics, 12, 3916. https://doi.org/10.3390/electronics12183916.

Al-Mashhadani; et al. (2024). Thermal Image Identification Against Pose and Expression Variations Using Deep Learning. Journal of Engineering Research, 12, 1–13. https://doi.org/10.1016/j.jer.2023.10.043.

Sharma, R.; et al. (2024). Low-Resolution Face Recognition: Review, Challenges and Research Directions. Computers and Electrical Engineering, 118, 109846. https://doi.org/10.1016/j.compeleceng.2024.109846.

Li, Y.; et al. (2024). Occluded Face Recognition Network Based on DCGAN and ResNet. Procedia Computer Science, 246, 1203–1212. https://doi.org/10.1016/j.procs.2024.09.087.

Viola, P.; Jones, M. (2004). Robust Real-Time Face Detection. In International Journal of Computer Vision, 57, 137–154. https://doi.org/10.1007/978-1-4757-4032-3_24.

Shorten, C.; Khoshgoftaar, T.M. (2019). A Survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6, 60. https://doi.org/10.1186/s40537-019-0197-0.

Sokolova, M.; Lapalme, G. (2009). A Systematic Analysis of Performance Measures for Classification Tasks. Information Processing and Management, 45, 427–437. https://doi.org/10.1016/j.ipm.2009.03.002.

Schroff, F.; Kalenichenko, D.; Philbin, J. (2015). FaceNet: A Unified Embedding for Face Recognition and Clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 815–823. https://doi.org/10.1109/CVPR.2015.7298682.

Goodfellow, I.; Bengio, Y.; Courville, A. (2016). Deep Learning; MIT Press: Cambridge, MA, USA.

Johnson, J.M.; Khoshgoftaar, T.M. (2019). Survey on Deep Learning with Class Imbalance. Journal of Big Data, 6, 27. https://doi.org/10.1186/s40537-019-0192-5.

Bengio, Y. (2012). Practical Recommendations for Gradient-Based Training of Deep Architectures. In Neural Networks: Tricks of the Trade, 2nd ed.; Springer: Berlin, Germany; pp. 437–478. https://doi.org/10.1007/978-3-642-35289-8_26.

Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15, 1929–1958.

Deng, J.; Guo, J.; Xue, N.; Zafeiriou, S. (2019). ArcFace: Additive Angular Margin Loss for Deep Face Recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 4690–4699. https://doi.org/10.1109/CVPR.2019.00482.

Pan, S.J.; Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22, 1345–1359. https://doi.org/10.1109/TKDE.2009.191.

Buda, M.; Maki, A.; Mazurowski, M.A. (2018). A Systematic Study of the Class Imbalance Problem in Convolutional Neural Networks. Neural Networks, 106, 249–259. https://doi.org/10.1016/j.neunet.2018.07.011.

Krawczyk, B. (2016). Learning from Imbalanced Data: Open Challenges and Future Directions. Progress in Artificial Intelligence, 5, 221–232. https://doi.org/10.1007/s13748-016-0094-0.

Smith, L.N. (2018). A Disciplined Approach to Neural Network Hyper-Parameters: Part 1—Learning Rate, Batch Size, Momentum, and Weight Decay. arXiv Preprint, arXiv:1803.09820.

Parkhi, O.M.; Vedaldi, A.; Zisserman, A. (2015). Deep Face Recognition. Proceedings of the British Machine Vision Conference (BMVC), 41.1–41.12. https://doi.org/10.5244/C.29.41.

Taigman, Y.; Yang, M.; Ranzato, M.; Wolf, L. (2014). DeepFace: Closing the Gap to Human-Level Performance in Face Verification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1701–1708. https://doi.org/10.1109/CVPR.2014.220.

Sun, Y.; Wang, X.; Tang, X. (2014). Deep Learning Face Representation from Predicting 10,000 Classes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1891–1898. https://doi.org/10.1109/CVPR.2014.244.

Shorten, C.; Khoshgoftaar, T.M. (2019). A Survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6, 60. https://doi.org/10.1186/s40537-019-0197-0.

Perez, L.; Wang, J. (2017). The Effectiveness of Data Augmentation in Image Classification Using Deep Learning. arXiv Preprint, arXiv:1712.04621

Schroff, F.; Kalenichenko, D.; Philbin, J. (2015). FaceNet: A Unified Embedding for Face Recognition and Clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 815–823. https://doi.org/10.1109/CVPR.2015.7298682.

Guo, Y.; Zhang, L.; Hu, Y.; He, X.; Gao, J. (2016). MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition. Computer Vision – ECCV 2016 Workshops, 87–102. https://doi.org/10.1007/978-3-319-46604-0_6.

Deng, J.; Guo, J.; Xue, N.; Zafeiriou, S. (2019). ArcFace: Additive Angular Margin Loss for Deep Face Recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 4690–4699. https://doi.org/10.1109/CVPR.2019.00482.

Buda, M.; Maki, A.; Mazurowski, M.A. (2018). A Systematic Study of the Class Imbalance Problem in Convolutional Neural Networks. Neural Networks, 106, 249–259. https://doi.org/10.1016/j.neunet.2018.07.011.

Deng, J.; Guo, J.; Niannan, X.; Zafeiriou, S. (2019). ArcFace: Additive Angular Margin Loss for Deep Face Recognition. CVPR 2019, 4690–4699. https://doi.org/10.1109/CVPR.2019.00482.

Cubuk, E.D.; Zoph, B.; Mane, D.; Vasudevan, V.; Le, Q.V. (2019). AutoAugment: Learning Augmentation Policies from Data. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 113–123. https://doi.org/10.1109/CVPR.2019.00020.

Tan, C.; Sun, F.; Kong, T.; Zhang, W.; Yang, C.; Liu, C. (2018). A Survey on Deep Transfer Learning. Lecture Notes in Computer Science, 11141, 270–279. https://doi.org/10.1007/978-3-030-01424-7_27.

Weiss, K.; Khoshgoftaar, T.M.; Wang, D. (2016). A Survey of Transfer Learning. Journal of Big Data, 3, 9. https://doi.org/10.1186/s40537-016-0043-6.

Read Full-Text
About this article

SUBMITTED: 17 May 2026
ACCEPTED: 16 June 2026
PUBLISHED: 20 June 2026
SUBMITTED to ACCEPTED: 30 days
DOI: https://doi.org/10.53623/gisa.v6i1.1197

Cite this article
Kurniawan, . A. ., & Hartati , E. . (2026). Preliminary Study of ResNet-Based Facial Identification for Access Control Systems. Green Intelligent Systems and Applications, 6(1), 161−173. https://doi.org/10.53623/gisa.v6i1.1197
Citations
0
Share this article