Abstract
COVID-19 is one of the diseases that causes a lot of trouble in social communication. To prevent the spread of the Covid-19, people must wear a mask during conversation, shopping, or studying. Therefore, it is necessary to develop an application that helps to verify whether people wear a face mask or not. Many approaches have been proposed to detect a facemask based on using machine learning-based methods, such as support vector machines, and convolutional neural networks. However, the performance of existing systems still has limitations under difficult deployment environments where the camera's quality is not good enough for detection. Therefore, in this research, we study the benefits of generative adversarial networks to produce a stable feature for robust deep learning model-based facemask detection. First, the generative adversarial network is used to learn and discover stable features from the input dataset. Second, the Yolo is then employed to learn the stable feature to effectively detect and recognize facemasks under various testing conditions. With comprehensive experimental results, we found that our proposed method achieved a detection rate of 84.45% by using the MaskedFace-Net dataset.
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The authors would like to express gratitude to Eastern International University (EIU) Vietnam.
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Nguyen, V.D., Debnath, N.C. (2023). Face Mask Detection by Using Deep Learning with the Generative Adversarial Network-Based Feature. In: Hassanien, A.E., Snášel, V., Tang, M., Sung, TW., Chang, KC. (eds) Proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 2022. AISI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 152. Springer, Cham. https://doi.org/10.1007/978-3-031-20601-6_13
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