Detecting face mask using eigenfaces and vanilla neural networks

Abstract

Coronavirus has <span>become one of the most deadly pandemics in 2021. Starting in 2019, this virus is now a significant medical issue all over the world. It is spreading extensively because of its modes of transmission. The virus spreads directly, indirectly, or through close contact with infected people. It is proclaimed that people should wear a mask in public areas as a counteraction measure, as it helps in suppressing transmission. A portion of the spaces, where the virus has broadly fanned out, is because of inappropriate wearing of facial cover. In crowded areas, keeping a check on facial masks manually is difficult. To automate this process, an effective and robust face mask detector is required. This paper discusses a hybrid approach using a machine learning technique called eigenfaces, along with vanilla neural networks. The accuracy was compared for three different values of principal components. The test accuracy achieved was 0.87 for 64 components, 0.987 for 512 components, and 0.989 for 1,000 components. Hence, this approach proved to be more promising and efficient than its counters.</span>

Sophisticated face mask dataset: a novel dataset for effective coronavirus disease surveillance

“…In [5], MobileNetV2 and YOLOv3 achieved 99% accuracy for mask detection and 94% for social distancing. As seen in [6], hybrid approaches combining eigenfaces and neural networks attained test accuracies of 0.87, 0.987, and 0.989 for varying components. Utilizing MobileNetV2, Hassan et al [7] developed a real-time mask recognition system on embedded devices with a recognition rate of 99%.…”