In the post-pandemic world, face masks have become essential. As a Full-Stack Developer exploring AI and Computer Vision, I decided to build a real-time face mask detection system using Python, OpenCV, and Deep Learning.
Here’s a breakdown of how I built it and what I learned.
🚀 Project Overview
The project captures live video from your webcam and uses a trained deep learning model (MobileNetV2) to determine if a person is wearing a mask or not. The model then overlays a label on the video feed in real-time.
🧠 Tools & Technologies Used
Python
OpenCV – for video capture and image processing
TensorFlow/Keras – for training the model
MobileNetV2 – a lightweight CNN model for fast inference
Matplotlib & NumPy – for data analysis and preprocessing
🛠️ How It Works
Data Collection
I used a dataset with two categories: with mask and without mask. You can find such datasets on Kaggle or build your own.
Model Training
I used MobileNetV2 as the base model and fine-tuned it with our dataset. After several epochs, the model reached over 95% accuracy.
Face Detection
Using OpenCV’s built-in face detector (cv2.CascadeClassifier), we detect faces in each video frame.
Prediction & Display
Each detected face is passed to the model. Based on the output, a green label (“Mask”) or red label (“No Mask”) is drawn on the frame.
📦 Key Learnings
Real-time applications require optimization — MobileNetV2 helped due to its lightweight architecture.
OpenCV is powerful for computer vision tasks, and integrating it with deep learning opens up many possibilities.
Proper dataset preparation (balanced, augmented, preprocessed) significantly boosts model performance.
📁 GitHub Repository
You can find the full code and instructions
📌 Final Thoughts
This was my first step into combining computer vision and deep learning for something impactful. It’s exciting to see how AI can be used in practical, real-time applications like this.
I’d love to hear your feedback! Let me know if you build on this or have suggestions for improvements. 👇
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