Real-Time Facemask Detection Using Deep Convolutional Neural Network-Based Transfer Learning

Jakaria Islam Emon, M. M. Manjurul Islam, Syeda Amina Abedin, Shakhawat Hossain, Rupam Kumar Das

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


In response to the rapid outbreak of COVID-19, this chapter presents an automated facemask detection technology in a real-time video stream using a deep convolutional neural network based transfer learning mechanism. In the training phase, visual geometry group network (VGG16) classifier is fine-tuned using a limited medical facemask dataset with weights sharing from a large pre-trained ImageNet weight model. To build an accurate model, VGG16 needs an abundance of data. Thus, we deploy a pre-trained model to be built on ImageNet, a large visual database, for fine-tuning the VGG16 model using knowledge transfer. In the testing phase, models are tested with offline images and video streams for classification performance measurements. To test the effectiveness of the proposed method on the real-time video stream, we set up the video acquisition using the webcam and pass each frame through the XML classifiers.
Original languageUndefined
Title of host publicationArtificial Intelligence for Disease Diagnosis and Prognosis in Smart Healthcare
PublisherTaylor & Francis
Number of pages12
ISBN (Print)9781003251903
Publication statusPublished (in print/issue) - 17 Feb 2023

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