Face Mask Detection Based on Machine Learning

Face Mask Detection Based on Machine Learning is a real-time, AI-driven solution designed to automatically determine whether individuals are wearing face masks correctly. The system leverages deep learning—a specialized branch within machine learning— by utilizing a pre-trained MobileNetV2 convolutional neural network for accurate and efficient mask classification. Integrated with computer vision libraries such as OpenCV, the application detects faces in live video streams and classifies them into three categories: “Mask,” “No Mask,” and “Incorrect Mask.” The training process involves transfer learning on a labeled dataset of masked and unmasked faces, enabling the model to generalize well across different facial orientations, lighting conditions, and occlusions. The face detection is performed using a Single Shot Detector (SSD) model, ensuring quick and reliable localization before classification. Designed for practical deployment in public places such as hospitals, schools, offices, and transportation hubs, the system ensures minimal latency and high accuracy, making it an effective tool for enforcing health compliance in the post-pandemic world. By applying deep learning within the broader framework of machine learning, this project demonstrates how intelligent systems can be built to address real-world health and safety challenges with precision and scalability KEYWORDS – Face Mask Detection, Machine Learning, Deep Learning, MobileNetV2, Convolutional Neural Network (CNN), Face Classification, Real-Time Detection, Transfer Learning, OpenCV, Computer Vision.