Pengenalan Wajah Bermasker dan Tidak Bermasker Menggunakan Artificial Neural Network Berdasarkan Haralick Tekstur Analisis. (Masked and Unmasked Face Recognition Using Artificial Neural Network Based on Haralick Texture Analysis).

Bijaksana, Raihan Arief (2024) Pengenalan Wajah Bermasker dan Tidak Bermasker Menggunakan Artificial Neural Network Berdasarkan Haralick Tekstur Analisis. (Masked and Unmasked Face Recognition Using Artificial Neural Network Based on Haralick Texture Analysis). Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

The COVID-19 pandemic has forced the global public to widely use face masks, which significantly affects the performance of traditional facial recognition systems. This research aims to overcome this challenge by developing a method that can accurately identify faces even if they are partially covered by a mask. We combine two powerful analysis approaches: Haralick texture features and maskless area analysis. The Haralick texture feature is used to extract texture details from facial images, while the Color Co-Occurrence Matrix (CCM) captures color variations that are important in facial recognition. To improve the efficiency and accuracy of the machine learning model, we use the WEKA tool to perform attribute selection, so that the model can focus on the most relevant features in masked and unmasked face conditions. This attribute selection also helps reduce data complexity and prevent overfitting. The results of this study show that the proposed method can effectively and reliably identify faces, both covered and without masks, and has the potential to be used in various security and public service applications in the post-pandemic era.

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