Face Mask Detection using Machine Learning
Aditya Chichghare, Nikhil Pathak, Nihar Kanse, Mayur Madhekar
OpenCV, Machine Learning, MobileNetV2, Keras, Haar Cascade
A face mask detection software reduces many efforts in the tasks which can be automated. A visual input is taken from a particular area using camera. Then using the predefined dataset, the visuals are compared to identify whether face mask is present or not. Once we have the visual form of the input, it can be given as an input to the deep learned model to predict the action or output that is supposed to be served to the user. The model is needed to have patterns in which it can distinguish between masked and non-masked people. The model will also consist of the actions to be taken, once it determines the presence of face mask. Also, a basic thing that this software requires is visual. For that, any camera like CCTV, wireless webcams, etc. can be used to capture visuals. Various python libraries like tensor flow, numpy, sklearn are used for implementation of machine learning. UI will consist of a window which will pop up on user’s command and it will display the visuals given by the camera to identify the face mask and then it will display the output accordingly whether face mask is present or not. Face mask detection software has many wide ranges of applications in this pandemic era.
Article Details
Unique Paper ID: 151875

Publication Volume & Issue: Volume 8, Issue 2

Page(s): 1124 - 1127
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