02024nas a2200253   4500000000100000000000100001008004100002260001200043653002500055653002400080653001600104653001100120653002200131100001700153700002000170700001800190700001700208245012000225856007900345300001000424490000600434520131600440022001401756       2022                            d  c12/202210aCoronavirus COVID-1910aMask Classification10aMobileNetV210aOpenCV10aTransfer Learning1 aAnubha Bhaik1 aVaishnavi Singh1 aEkta Gandotra1 aDeepak Gupta00aDetection of Improperly Worn Face Masks using Deep Learning – A Preventive Measure Against the Spread of COVID-19  uhttps://www.ijimai.org/journal/sites/default/files/2022-11/ijimai7_7_2.pdf  a14-250 v73 aCoronavirus disease 2019 has had a pressing impact on people all around the world. Ceasing the spread of this infectious disease is the urgent need of the hour. A vital method of protection against the virus is wearing masks in public areas. Not merely wearing masks but wearing masks properly can ensure that the respiratory droplets do not get transmitted to other people. In this paper, we have proposed a deep learning-based model, which can be used to detect people who are not wearing their face masks properly. A convolutional neural network model based on the concept of transfer learning is trained on a self-made dataset of images and implemented with light-weighted neural network called MobileNetV2 for mobile architectures. OpenCV is used with Caffe framework to detect faces in an input frame which are further forwarded to our trained convolutional neural network for classification. The method has been implemented on various input images and classification results have been obtained for the same. The experimental results show that the proposed model achieves a testing accuracy and training accuracy of 93.58% and 92.27% respectively. Optimal results with high confidence scores and correct classification have also been achieved when the proposed model was tested on individual input images.  a1989-1660