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.