PENDETEKSIAN PENGGUNAAN MASKER WAJAH DENGAN METODE CONVOLUTIONAL NEURAL NETWORK

Bunardi Budiman, Chairisni Lubis, Novario Jaya Perdana

Abstract


“Face Mask Detection Using the Convolutional Neural Network” is a PC based program that aims to detect and classify human beings whether a person is using a mask or not with access through a webcam camera.  This program is created using the Python language with several libraries. The classification of face masks uses the Convolutional Neural Network method with the MobileNetV2 architecture. Meanwhile, human face detection uses the Haarcascade Classifier. How the program works is by accessing the connected camera and if the person detected is wearing a mask, the person will be labeled "using a mask" and given a green box to mark the detection along with the analysis value, whereas if not, it will be labeled "not using a mask" and a red box with also the predicted value. From the test results, it can be proven that the accuracy program is good enough to detect the use of face masks with an average object detection accuracy of 88.53% and the classifier for the use of mask an average of 84.45%.


Keywords


Convolutional Neural Network, Face Mask, Haarcascade Class, Python

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References


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