An Application of Detecting Faces with Mask and without Mask using Deep Learning Model

Abstract

: The proposed model is stronger as it naturally will identify people with masks and without mask. This approach reduces the deep learning process to a single stage and the mask detector model is added to identify with mask and without mask. What we need to do is to use the learning algorithm to provide us with bounding cases in one forward network pass for both people with masks and without masks. The Keras classifier is based on the MobileNetV2 neural net architecture. This model was tested in real time with pictures and video streams. Although the exactness of the prototype is around 98% and model optimisation is a continuous process by setting the hyper-parameters. We are finding a highly precise solution. Size and computer costs are highly optimized and tailored for object detection tasks on-device such as a cell phone or camera streams.