Face Mask Detection Project Guide

During health crises like the COVID-19 pandemic, ensuring proper face mask usage in public spaces became crucial. Manual monitoring is tedious and not scalable. An AI-powered system can detect whether a person is wearing a mask correctly by analyzing live video feeds. Implementing such systems improves public safety, compliance, and reduces the burden on human security personnel, showcasing the practical power of computer vision.

By using a face detector combined with a Convolutional Neural Network (CNN) classifier, you can detect masked and unmasked faces in real time. Models like MobileNetV2, SSD, or YOLO are commonly used for this task. Data augmentation improves robustness against different face orientations, occlusions, and lighting conditions. Building this system enhances your skills in object detection, real-time processing, and smart surveillance technologies.

The system uses a face detection model to locate faces within each video frame. A CNN classifier then analyzes the detected face region to determine if the person is wearing a mask properly or not. Bounding boxes are drawn around faces with labels like 'Mask' or 'No Mask.' Datasets like RMFD or custom datasets created through web scraping provide ample training data for both masked and unmasked cases under different conditions.

• Collect datasets like RMFD (Real-World Masked Face Dataset) or generate custom datasets for masked/unmasked faces.
• Preprocess: resize face regions, normalize pixel values, and augment with random occlusions or rotations.
• Train CNN models like MobileNetV2 or lightweight YOLO variants for binary classification (mask/no-mask).
• Evaluate using accuracy, precision, recall, and real-time inference speed (FPS) metrics.
• Deploy the system onto live webcam streams or surveillance footage for real-time mask compliance monitoring.

• Real-World Masked Face Dataset (RMFD)
• OpenCV Video Streaming Documentation
• TensorFlow Face Mask Detection Guide