This project implements a deep learning based face mask detection system using a convolutional neural network. The system automatically identifies whether a person is wearing a face mask or not by analyzing facial image data.
The project demonstrates how computer vision and deep learning can be applied to real world public health and safety applications.
Project Objective
The objective of this project is to build an automated image classification system capable of detecting face mask usage. The model aims to accurately classify images into masked and unmasked categories using learned visual features.
This project highlights the role of deep learning in monitoring and safety enforcement systems.
What This Project Does
The system processes facial images and extracts spatial features using convolutional layers. Based on these features the neural network predicts whether a face mask is present.
The workflow includes image preprocessing model training evaluation and prediction.
Dataset Description
The dataset contains labeled images of faces with masks and without masks. Images are captured under different lighting conditions face orientations and backgrounds to improve model robustness and generalization.
CNN Architecture
The convolutional neural network consists of multiple convolution and pooling layers designed to capture facial patterns. Fully connected layers perform final classification based on extracted features.
Binary classification is achieved using a sigmoid activated output layer.
Training and Evaluation
The model is trained on a labeled dataset with validation monitoring to reduce overfitting. Performance is evaluated on unseen images to assess classification accuracy and generalization capability.
Key Highlights
- Binary image classification using CNN
- Automated face mask detection system
- Robust preprocessing pipeline
- End to end deep learning workflow
- Suitable for real world safety applications
Technologies Used
Python for implementation
TensorFlow and Keras for deep learning modeling
NumPy for numerical operations
Image data generators for preprocessing
Matplotlib for visualization
Use Case and Applications
This project can be used in public surveillance systems workplaces healthcare environments and transportation hubs to monitor safety compliance.
The approach can be extended to real time video based detection systems.