Abstract
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.
Highlights
The COVID-19 pandemic is still ongoing until 2021 and is likely to continue until an uncertain time
The test results using the adjacent evaluation local binary patterns (AELBP) method show that the accuracy and F-measure are 98.39% and 98.08%, respectively
Min, “Completed robust local based on skin color characteristic and AdaBoost algorithm,” J. binary pattern for texture classification,” Neurocomputing, vol
Summary
Virus SARS-CoV-2 telah mengakibatkan pandemi di seluruh dunia. Virus SARS-CoV-2 mengakibatkan penyakit yang disebut dengan COVID-19. Maka Banyak penelitian lain pada bidang analisis wajah orang tersebut akan diingatkan untuk mengenakan menggunakan informasi tekstur sebagai ciri atau feature. Terdeteksi ada seseorang yang ingin masuk ke suatu mengombinasikan local binary patterns dan tempat tanpa mengenakan masker, maka alarm dapat convolutional neural networks (CNN) untuk melakukan berbunyi secara otomatis. Ini menjadi tantangan tersendiri karena model, warna, dan motif masker yang digunakan oleh masyarakat [13] menggunakan local binary patterns pada kasus pengenalan wajah tiga dimensi. Pada penelitian ini, informasi tekstur digunakan sebagai ciri atau feature untuk membedakan antara wajah yang mengenakan dan tidak mengenakan masker. Banyak penelitian pada penelitian ini karena dataset yang digunakan berisi yang mencoba untuk memodifikasi metode local binary citra-citra yang telah melewati proses deteksi wajah patterns untuk meningkatkan kualitas ciri yang tersebut. Hasil akhirnya adalah informasi apakah wajah pada citra input menggunakan masker atau tidak
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