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After series of waves of Corona virus, the disease’s pandemic stage for the majority of nations throughout the world, COVID-19 doesn’t seem to be ending anytime soon. In this difficult time of the COVID-19, people frequently wear masks to keep themselves safe and to minimize the transmission of corona virus. Face identification is a particularly difficult task when a portion of the face is hidden. Hence, it is essential to enhance the efficiency of the existing system’s facial recognition technology when dealing with individuals wearing masks. Our goal is to develop a real-time Graphical User Interface (GUI) based Automated Facial Recognition and Mask Detection System capable of identifying and recognizing individuals wearing face masks in both pre-recorded videos and images, as well as real-time scenarios. The suggested methodology uses the HAAR Cascade and Principal Component Analysis (PCA) methods. A coloured box then displays the result, indicating whether or not the subject of the camera is donning a mask. This copy’s accuracy is 99.5%.

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Real Time Facemask Detection Using Deep Learning to Tackle COVID-19

  • 会议论文

  • 发表日期：

    2025年

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  • R. Virupaksha Gouda,
  • Anuradha Suresh,
  • Manjunath,
  • R. M. Jagadish

摘要

After series of waves of Corona virus, the disease’s pandemic stage for the majority of nations throughout the world, COVID-19 doesn’t seem to be ending anytime soon. In this difficult time of the COVID-19, people frequently wear masks to keep themselves safe and to minimize the transmission of corona virus. Face identification is a particularly difficult task when a portion of the face is hidden. Hence, it is essential to enhance the efficiency of the existing system’s facial recognition technology when dealing with individuals wearing masks. Our goal is to develop a real-time Graphical User Interface (GUI) based Automated Facial Recognition and Mask Detection System capable of identifying and recognizing individuals wearing face masks in both pre-recorded videos and images, as well as real-time scenarios. The suggested methodology uses the HAAR Cascade and Principal Component Analysis (PCA) methods. A coloured box then displays the result, indicating whether or not the subject of the camera is donning a mask. This copy’s accuracy is 99.5%.