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Face Mask Detection Using Deep Learning

COVID-19 is a newly identified corona virus that causes an infectious sickness. Masks are a vital tool for preventing the spread of Covid-19 and saving lives. Because the wearing of mask has become one of the non-pharmaceutical intervention approaches that could be used to prevent SARS-CoV2 droplets from covering infected patients’ noses and mouths and being released in public, it should be used. In tests, wearing a face mask has been proven to significantly reduce viral transmission and provide a sensation of protection. The proposed technique uses Deep Convolutional Neural Networks (DCNN) with layers to test the suggested technique for binary classification with average pooling layer, flattening layer, dense ReLU layer and decisive layer with a soft max activation function using three models such as ResNet50, AlexNet and Mobilenet. The proposed method can be integrated into Facial landmarks with face mask using Multi-Task Cascaded Convolutional Neural Network, which is the most accurate face detection approach (MTCNN). Here, InceptionV3 and VGG 16 are two pre-trained models that are used to detect the face mask. The two models are compared based on the performance and it is found from the results that VGG 16 offers better accuracy than InceptionV3.