Face Mask Detection Using Convolutional Neural Networks

Face mask detection has become a critical task in the current scenario to ensure public safety and prevent the spread of infectious diseases. In this project, we propose a deep learning approach using convolutional neural networks (CNNs) to detect whether a person is wearing a face mask or not. The dataset used for training and evaluation consists of images of individuals with and without face masks. The images are collected from various sources and carefully labelled to ensure accuracy. Each image is pre processed to enhance the features and normalize the pixel values. The proposed CNN architecture consists of multiple convolutional layers, followed by pooling and fully connected layers. The convolutional layers extract relevant features from the input images, while the pooling layers reduce the spatial dimensions of the feature maps. The fully connected layers classify the images into two categories: with face mask and without face mask. To train the CNN model, we use a combination of supervised learning techniques and data augmentation. We employ various optimization algorithms to minimize the classification loss and improve the accuracy of the model. In conclusion, this project presents a deep learning based approach for face mask detection using convolutional neural networks. The proposed model demonstrates promising results in accurately identifying individuals wearing face masks. The project contributes to the development of intelligent systems for public health and safety