Identification of Proper Use of Masks Based on Mouth and Nose Detection
Abstract
During the pandemic, the proper use of face masks is very critical to reduce and prevent the transmission of the COVID-19 between people. Unfortunately, many people are still careless about this proper use of masks like using masks to cover only their mouth or chin. There are also people who do not wear masks when traveling or interacting. Such conducts indicate the lack of concern for the mask use. This study aims to obtain an identification algorithm for using the face mask appropriately through digital photos/images. The basic algorithm used was the face, nose, and mouth detection algorithm developed by Viola and Jones. These algorithms were then combined so that they formed a strong algorithm for detecting the proper use of the face mask. The data tested were classified into five categories, namely images of proper use of masks, images of masks with visible noses, images of masks with visible mouths, images of faces with masks worn on the chin, and mixed images with various accessories. Results of the study employed sixty testing images with various variations of attributes, the result obtained an accuracy value of 90%, a sensitivity value of 100%, and a specificity value of 62.5%. The low specificity value was caused by many detection errors in the false positive (FP) attribute, meaning that the system can detect objects other than the mouth and nose. This research is expected to be developed and synergized with other applications so that it can raise public awareness about the proper use of masks.
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