Face Mask Detection using CNN

Introduction

Face mask detection is a crucial task in ensuring public health and safety, especially during pandemics like COVID-19. This project aims to develop a convolutional neural network (CNN) model to detect whether a person is wearing a face mask or not. The CNN model is trained on a dataset containing images of people with and without face masks.

Dataset

The dataset used in this project is the "Face Mask Dataset", which consists of images of individuals wearing and not wearing face masks. The dataset is divided into three subsets: train, validation, and test. Consisting of 11,792 images belonging to 2 classes.

Data Augmentation

To enhance the diversity of the training dataset and improve the generalization ability of the model, data augmentation techniques are applied. These techniques include:

• Rotation

• Width and height shift

• Shear

• Zoom

• Horizontal flip

After data augmentation, the dataset distribution by label is illustrated in the following chart.

Experimentation

Model Training

Several pre-trained CNN architectures are utilized in this project, including VGG16, VGG19, Xception, ResNet50, MobileNetV2, and DenseNet121. Pre-trained models are fine-tuned on the face mask dataset.

Model Comparison

A comparison is conducted to assess the effectiveness of each model in detecting face masks. The test accuracy and other relevant metrics are analyzed to determine the best-performing model.

The following is the performance chart of the models on the test set.

Conclusion

In conclusion, this project demonstrates the application of CNN models for face mask detection. By leveraging pre-trained architectures and data augmentation techniques, we achieve promising results in detecting face masks accurately.