This project implements a real-time face mask detection system using deep learning techniques. It utilizes a Convolutional Neural Network (CNN) to classify individuals as wearing a mask or not, processing live video streams to ensure compliance with health guidelines.
- Real-Time Detection: Processes live video feeds to detect face masks instantaneously.
- High Accuracy: Employs a trained CNN model to achieve reliable detection results.
- User-Friendly Interface: Simple setup and execution for immediate use.
- Clone the Repository:
git clone https://github.com/anandharshit712/Real_Time_Mask_Detection.git cd Real_Time_Mask_Detection - Install Dependencies:
pip install -r requirements.txt
- Run the Detection Script:
python detect_mask_video.py
- Operation:
- Ensure your webcam is connected.
- The system will display a live video feed with bounding boxes around detected faces, indicating "Mask" or "No Mask".
- Press 'q' to exit the application.
The model is trained on a dataset comprising images of individuals with and without face masks. The dataset includes both real and augmented images to enhance model robustness. For more information, refer to the Masked Face Recognition Dataset and Application.
The face mask detector is built using a Convolutional Neural Network (CNN) architecture. The model was trained using TensorFlow and Keras, achieving high accuracy in distinguishing between masked and unmasked faces.
This project is inspired by and adapted from various open-source face mask detection projects, including:
- Real-Time Face Mask Detection by naemazam
- Real-Time Face Mask Detection by AliElneklawy
- Face Mask Detector by Karan-Malik
This project is licensed under the MIT License.
Special thanks to the contributors of the datasets and open-source projects that made this work possible.
Note: This README provides an overview of the Real-Time Face Mask Detection project. For detailed information, please refer to the project's documentation and source code.