Abstract:
Computer technology used in various programs to recognize people's faces in digital
images is apprehended as facial recognition. There is a wide range of applications in the
fields of content-based content retrieval, video encoding, video conferencing, crowd
viewing, and smart computer interaction. Object detection is a computer vision approach
that allows us to recognize and locate objects in an image or video. The main objective of
this research was to develop a security system that could detect a face mask, a person's
face, and the number of people standing in front of the camera in real-time using realtime video capture. OpenCV is a cross-platform library of over 2500 algorithms that have
been optimized and were chosen for our project due to its benefits. One of the most basic
machine learning-based methods is the Haar cascade classifier. Haar features are
particularly useful for mask detection because they are excellent at detecting edges and
lines, simultaneously, cascade classifiers are one of the few real-time algorithms
available. The accuracy level was very poor when a built-in Haar cascade was used for
the project, to solve this issue, a new Haar cascade was trained to improve accuracy
which was proved to be effective. Three separate features, such as face mask, face, and
human detection, have been brought under one project. A GUI was created using the
Tkinter module to achieve that. Face mask detection, face detection, and human detection
are the three key features of this project to ensure social distancing digitally. This
designed system can be used effectively in applications like, keeping any location
protected from intruders, ensuring face-mask wearing, and preserving social distance
