An efficient model for detecting real-time facemask based on different Classification Algorithms

• Published in: Multimedia tools and applications 2024-05, Vol.83 (18), p.55175-55198
• Main Authors: Singh, Nutan, Tripathi, Priyanka
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Classification; Computer Communication Networks; Computer Science; Computer vision; Coronaviruses; COVID-19; Data Structures and Information Theory; Datasets; Deep learning; Disease transmission; Fatalities; Machine learning; Multimedia; Multimedia Information Systems; Pattern recognition systems; Public health; Real time; Special Purpose and Application-Based Systems; Track 2: Medical Applications of Multimedia
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-023-17634-6

Nowadays, there is such an outbreak of corona in the whole world that people are taking many precautions to avoid it, but according to the Guidelines of WHO, wearing a mask and maintaining distance is the best way to escape from this epidemic. It has been said that those who do not wear facemasks are at risk of infection. In technology, computer vision-based facemask detection uses a different learning algorithm to recognize face masks in real time. This research compares the performance of seven machine learning, deep learning, and transfer learning algorithms, such as K-NN, SGD, SVM, CNN, MobileNetV2, InceptionV3, and VGG-19, to discover the best algorithm for detecting who is wearing a masked face in a real-time environment. In addition, we got a higher accuracy of 100% in the InceptionV3 algorithm, that InceptionV3 tends to be faster when compared to another algorithms.