A Framework to Recognize Multiple Heterogeneous Face-Masks in Real Time using FusionNet

COVID-19 pandemic has had a dramatic impact on our daily lives, disrupting global trade and transportation. Protecting one's face with a mask has become the new normal. In the present new normal situation and in near future also, customers must wear masks properly in many public services. Therefore, face mask identification has become a necessary and challenging stuff in the present new normal scenario. In this paper, a computer vision-based method using proposed convolution neural network (CNN), named as FusionNet is introduced to correctly detect the face mask. The proposed FusionNet architecture is constructed by combining of VGG16Net with MobileNetV2. This approach accurately recognizes the face mask from a video sequence and determines whether a person wearing a mask or not. The method is evaluated on the dataset which consist of total 400 images (200 images with mask and 200 images without mask) captured from different social gatherings. The proposed method can also be used to detect multiple faces with or without mask in a single video frame and achieves 99.75% accuracy which is better compared to individual performance of using standard VGG16Net and MobileNetV2.