Occluded Face Recognition with Deep Learning

Qin, Jiayu

doi:10.1007/978-981-16-8885-0_3

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…When the entire face is not visible, occluded face identification becomes significantly more difficult. Jiayu ( 69 ) observed that the problem has not been fully resolved and that it has remained a hot topic in recent years. In this study, the real-world masked face dataset was evaluated on five advanced models with the goal of comparing their performance.…”

Section: State Of the Art Modelsmentioning

confidence: 99%

Deep learning techniques for detecting and recognizing face masks: A survey

Alturki

Alharbi

AlAnzi

et al. 2022

Front. Public Health

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The year 2020 brought many changes to the lives of people all over the world with the outbreak of COVID-19; we saw lockdowns for months and deaths of many individuals, which set the world economy back miles. As research was conducted to create vaccines and cures that would eradicate the virus, precautionary measures were imposed on people to help reduce the spread the disease. These measures included washing of hands, appropriate distancing in social gatherings and wearing of masks to cover the face and nose. But due to human error, most people failed to adhere to this face mask rule and this could be monitored using artificial intelligence. In this work, we carried out a survey on Masked Face Recognition (MFR) and Occluded Face Recognition (OFR) deep learning techniques used to detect whether a face mask was being worn. The major problem faced by these models is that people often wear face masks incorrectly, either not covering the nose or mouth, which is equivalent to not wearing it at all. The deep learning algorithms detected the covered features on the face to ensure that the correct parts of the face were covered and had amazingly effective results.

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Section: State Of the Art Modelsmentioning

confidence: 99%