A health crisis is raging all over the world with the rapid transmission of the novel-coronavirus disease (Covid-19). Out of the guidelines issued by the World Health Organisation (WHO) to protect us against Covid-19, wearing a facemask is the most effective. Many countries have necessitated the wearing of face masks, but monitoring a large number of people to ensure that they are wearing masks in a crowded place is a challenging task in itself. The novel-coronavirus disease (Covid-19) has already affected our day-to-day life as well as world trade movements. By the end of April 2021, the world has recorded 144,358,956 confirmed cases of novel-coronavirus disease (Covid-19) including 3,066,113 deaths according to the world health organization (WHO). These increasing numbers motivate automated techniques for the detection of a facemask in real-time scenarios for the prevention of Covid-19. We propose a technique using deep learning that works for single and multiple people in a frame recorded via webcam in still or in motion. We have also experimented with our approach in night light. The accuracy of our model is good compared to the other approaches in the literature; ranging from 74% for multiple people in a nightlight to 99% for a single person in daylight.

Like everyone else who got to test Apple's new Vision Pro after its unveiling at the Worldwide Developers Conference in Cupertino, California, this week, I couldn't wait to experience it. But when an Apple technician at the ad hoc test facility used an optical device to check out my prescription lenses, I knew that there might be a problem. The lenses in my spectacles have prisms to address a condition that otherwise gives me double vision. Apple has a set of preground Zeiss lenses to handle most of us who wore glasses, but none could address my problem. In any case, my fears were justified: When I got to the demo room, the setup for eye-tracking--a critical function of the device--didn't work. I was able to experience only a subset of the demos.

Currently, there are no specific guidelines on the most effective materials and designs for facemasks to minimize the spread of droplets from coughs or sneezes to mitigate the transmission of COVID-19. While there have been prior studies on how medical-grade masks perform, data on cloth-based coverings used by the vast majority of the general public are sparse. Research from Florida Atlantic University's College of Engineering and Computer Science, just published in the journal Physics of Fluids, demonstrates through visualization of emulated coughs and sneezes, a method to assess the effectiveness of facemasks in obstructing droplets. The rationale behind the recommendation for using masks or other face coverings is to reduce the risk of cross-infection via the transmission of respiratory droplets from infected to healthy individuals. Researchers employed flow visualization in a laboratory setting using a laser light sheet and a mixture of distilled water and glycerin to generate the synthetic fog that made up the content of a cough-jet.