In the midst of the ongoing COVID-19 pandemic, adherence to safety measures such as wearing masks has become crucial. To assist in enforcing maskwearing norms without relying on manual intervention, we present a mask detection system that utilizes a Convolutional Neural Network (CNN) and facial recognition algorithm. Our system compares images of faces with and without masks, automatically detects the presence or absence of masks, and triggers an alert mechanism for the general public. In this report, we address the challenge of detecting masked face regions by proposing a novel approach that involves discarding the masked region and leveraging deep learning-based features. By focusing on the uncovered facial areas, our model aims to improve accuracy and efficiency in mask detection. Through the utilization of CNN, our system learns discriminative features from the face images, enabling it to accurately classify whether an individual is wearing a mask or not. The facial recognition algorithm further enhances the system's ability to identify and track individuals in real-time, facilitating quick and reliable detection. To evaluate the effectiveness of our proposed model, we conducted extensive experiments on a diverse dataset containing images of individuals wearing masks. The results demonstrate the system's high accuracy and efficiency in detecting masks, even in challenging scenarios such as varied lighting conditions and different mask types. Our mask detection system holds significant potential for deployment in various public spaces, such as airports, train stations, and shopping malls, to help ensure compliance with mask-wearing guidelines. By automating the detection process, the system can contribute to reducing the spread of COVID-19 and protecting public health.

Keywords : Mask Detection, Convolutional Neural Network (CNN), Facial Recognition, COVID-19, Safety Measures