Face Mask Detection Using Keras This project is a Python-based face mask detection system implemented with Keras, TensorFlow, and OpenCV. It identifies whether a person is wearing a mask or not, using a trained deep learning model.
Features Real-time mask detection using a webcam or video feed. Pre-trained model built on a labeled dataset for accurate classification. Simple and easy-to-use implementation with Python and OpenCV. Credits and Inspiration This project is built upon the dataset and ideas from the following contributors and resources:
Prajna Bhandary: Original dataset, available on GitHub. Adrian Rosebrock: Tutorials and guides on deep learning for computer vision. sentdex: Educational resources on AI and Python. Video Tutorial: Face Mask Detection on YouTube. Technology Stack Deep Learning Framework: Keras and TensorFlow Computer Vision Library: OpenCV Language: Python Dataset The dataset consists of labeled images of people with and without masks, prepared by Prajna Bhandary. It forms the foundation for training the detection model.
Installation Clone the repository:
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git clone https://github.com/yourusername/face-mask-detection-keras.git
cd face-mask-detection-keras
Install dependencies:
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pip install -r requirements.txt
Run the script to test detection:
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python detect_mask.py
How It Works
The model is trained using a labeled dataset of images with and without face masks.
OpenCV is used to preprocess input images and video feeds.
The trained deep learning model predicts mask presence in real time.
Future Enhancements
Adding support for edge devices like Raspberry Pi.
Improving accuracy by training on larger datasets with diverse populations.
Extending functionality to detect proper mask usage (e.g., masks covering the nose).
License
This project is licensed under the MIT License.
Bringing safety and awareness through AI and computer vision. 🚀