A Comparative Study of Embedded Learning Models IoT-Based For Real time Mask Detection - Mohamed Amine Meddaoui; Mohammed Erritali; françoise Sailhan (Utan Kayu Publishins, 2024)
Following the outbreak of the coronavirus, many preventive measures are implemented to slow down the transmission of the virus. Amongst others, face mask detection is a key innovative technology that allows the identification of the number of individuals wearing face masks. In this regard, this paper provides a comparative study of several machine learning and deep learning algorithms (e.g., SVM, RNN, Mask-RCNN, LSTM, CNN, Auto-Encoder, GAN, U-Net GAN) that support mask detection