Face Mask Detection Using LabView

Chella Santhosh, M Ravi Kumar, J Lakshmi Prasanna, I. Ram Kumar, U Vinay Kumar, S Navya Sri


Rapid worldwide spread of Corona virus Disease 2019 (COVID 19) has resulted in a global pandemic. In present scenario due to covid-19, the mask has been an important part of our live for our safety as well as for the others safety so there is a need for efficient face mask detection applications in crowded areas like shopping malls, Public transportation etc. To ensure safety of the people in the surroundings. Face Mask Detection using NI LabVIEW. In this project a real-time system is developed to detect whether the person is wearing a mask or not by acquiring a real-time image of him through a Camera. The main challenges in detecting the mask are there are masks with various colors and patterns and secondly the background, light intensity are also the factors that affect the result. So, all these factors should be taken into consideration while developing the system in real-time. This system used for this application consists of vision development module. Vision development module helps to develop applications for machine vision and image processing applications we can use it with LabVIEW for real- time systems. A camera with good pixel quality is used for image acquisition. The captured image is of RGB format, it is difficult to analyze the image in this format, so it undergoes color plane extraction in this only a single plane of the image is considered which separates the mask from surroundings and results in a grey scale image for further processing. The image later is compared to a custom-made template dataset using pattern matching algorithm from vision assistant which helps to detect the mask region. overlaying techniques are used to highlight the mask region which shows that the person is wearing the mask.


Face Mask Detection; LabVIEW; Vision Development Module; Pattern Matching; Overlaying; Color plane extraction.

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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