Real-Time Face Mask Detection Using Transfer Learning with MobileNetV2
Abstract
The COVID-19 pandemic created an urgent need for automated systems capable of verifying face-mask compliance in public spaces. This paper presents a lightweight, real-time face-mask classifier built on transfer learning with the MobileNetV2 architecture. A pretrained ImageNet backbone is used as a frozen feature extractor with a compact classification head, followed by a fine-tuning phase that unfreezes the final convolutional layers. The model is trained and evaluated on the publicly available. Face Mask ∼12K Images dataset, comprising approximately twelve thousand pre-cropped and class-balanced face images split into training, validation, and test partitions. Using data augmentation, two-phase training, and standard regularization, the classifier attains approximately 99% accuracy on the held-out test set with near-perfect precision and recall for both the masked and unmasked classes. The results confirm that a low-compute, mobile-oriented backbone combined with transfer learning is sufficient for accurate binary mask detection, making the approach suitable for deployment on edge devices. The proposed pipeline is a clean, reproducible, end-to-end implementation rather than a novel methodology.
References
[2] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv:1409.1556, 2014.
[3] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2016, pp. 770–778.
[4] C. Szegedy et al., “Going deeper with convolutions,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2015, pp. 1–
9.
[5] A. G. Howard et al., “MobileNets: Efficient convolutional neu-ral networks for mobile vision applications,” arXiv:1704.04861, 2017.
[6] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted residuals and linear bottlenecks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2018,
pp. 4510–4520.
[7] J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transfer-able are features in deep neural networks?” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), 2014.
[8] P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2001.
[9] K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, “Joint face detec-tion and alignment using multitask cascaded convolutional net-works,” IEEE Signal Process. Lett., vol. 23, no. 10, pp. 1499–1503, 2016.
[10] M. Loey, G. Manogaran, M. H. N. Taha, and N. E. M. Khalifa, “A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic,” Measurement, vol. 167, art. 108288, 2021.
[11] M. Jiang, X. Fan, and H. Yan, “RetinaFaceMask: A face mask detector,” arXiv:2005.03950, 2020.
[12] A. Rosebrock, “COVID-19: Face mask detector with OpenCV, Keras/TensorFlow, and deep learning,” PyImageSearch, 2020. [Online]. Available: https: //pyinagesearch. con/
[13] A. Jangra, “Face Mask ∼12K images dataset,” Kaggle, 2020. [Online]. Available: https: //vvv. kaggle. con/datasets/ ashishj angra27/face- nask- 12k- inages- dataset
[14] A. Cabani, K. Hammoudi, H. Benhabiles, and M. Melkemi, “MaskedFace-Net—A dataset of correctly/incorrectly masked face images in the context of COVID-19,” Smart Health, vol. 19, art. 100144, 2021.
[15] Z. Wang et al., “Masked face recognition dataset and applica-tion,” arXiv:2003.09093, 2020.
Copyright (c) 2026 International Journal of Artificial Intelligence & Mathematical Sciences
This work is licensed under a Creative Commons Attribution 4.0 International License.
