Face Mask Detection System Using Machine Learning Algorithms

Abstract

The project presented in this report is a real-time face mask detection system using computer vision and deep learning techniques. The primary objective of this project is to develop a system that can detect whether a person is wearing a face mask or not, with a focus on real-time performance.
 The system utilizes pre-trained deep learning models for face detection and mask classification. It leverages the MobileNetV2 architecture as a feature extractor and deploys the model in real-time video streams. When a face is detected, the system classifies it as "Mask" or "No Mask" with associated confidence scores. The project involves key components, including the use of OpenCV for image processing and real-time video capture, TensorFlow/ Keras for deep learning, and the integration of pre-trained models. The code is well- structured, and it demonstrates proficiency in model loading, image preprocessing, and real-time video processing.
 The findings of the project showcase a practical application for face mask detection, which has gained significance in the context of public health and safety. The system provides a valuable tool for monitoring mask compliance in public spaces and can contribute to efforts to mitigate the spread of contagious diseases. The project demonstrates the importance of combining computer vision and deep learning in real-world applications, and it serves as a reference for those interested in similar projects or applications in the field of image processing and object detection. In summary, this project illustrates the successful implementation of a real-time face mask detection system and underscores its potential contributions to public health and safety measures.