  In response to the global COVID-19 pandemic, there has been a critical demand
for protective measures, with face masks emerging as a primary safeguard. The
approach involves a two-fold strategy: first, recognizing the presence of a
face by detecting faces, and second, identifying masks on those faces. This
project utilizes deep learning to create a model that can detect face masks in
real-time streaming video as well as images. Face detection, a facet of object
detection, finds applications in diverse fields such as security, biometrics,
and law enforcement. Various detector systems worldwide have been developed and
implemented, with convolutional neural networks chosen for their superior
performance accuracy and speed in object detection. Experimental results attest
to the model's excellent accuracy on test data. The primary focus of this
research is to enhance security, particularly in sensitive areas. The research
paper proposes a rapid image pre-processing method with masks centred on faces.
Employing feature extraction and Convolutional Neural Network, the system
classifies and detects individuals wearing masks. The research unfolds in three
stages: image pre-processing, image cropping, and image classification,
collectively contributing to the identification of masked faces. Continuous
surveillance through webcams or CCTV cameras ensures constant monitoring,
triggering a security alert if a person is detected without a mask.
