Deteksi Masker Wajah Menggunakan Metode Adjacent Evaluation Local
Binary Patterns

Deteksi Masker Wajah Menggunakan Metode Adjacent Evaluation Local
Binary Patterns

classification, COVID-19, face mask

The COVID-19 pandemic is still ongoing until 2021 and is likely to continue until an uncertain time. This arises because the
spread of the SARS-CoV-2 virus also continued to occur in the community. Of the five points in 5M that has been initiated by
the government, the focus of this study is the use of face masks. In this study, an image-based automatic mask detection method
using a classification approach is proposed. This method can be used in automated systems to increase public discipline in
wearing masks to suppress the spread of the SARS-CoV-2 virus. The classes used in the classification are "with mask" and
"without mask". The adjacent evaluation local binary patterns (AELBP) method, which is an extension of the local binary
patterns (LBP) method, is used to extract the texture features of each image. Tests were carried out on 2,172 facial images of
various sizes, facial accessories, and facial expressions. The test results using the AELBP method show that the accuracy and
F-measure are 98.39% and 98.08%, respectively. This result is better than other methods which are also evaluated. In addition,
testing of the AELBP method execution time shows that this method is feasible to use on real systems.

Randy Cahya Wihandika

Universitas Brawijaya

: 20-08-2021

Fajar Bagus W

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Indonesia

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