Volume 20 No 12 (2022)
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An Efficient Masked-Face Recognition Model during COVID-19 using Artificial Intelligence Techniques
Graceline Jasmine S, Rukmani.P , Prabhu Natarajan, Vergin Raja Sarobin M, Febin Daya J L, Dhanya D
The COVID-19 is a modern-day crisis unparalleled in its effects leading to an enormous number of sufferers and security problems. People frequently use masks to guard themselves and lessen the spread of the virus. This makes face recognition a very challenging task since certain portions of the face, vital for recognition, are unseen. Face detection has become an important aspect with respect to safety and security and is also widely used in Image processing and Computer Vision. Several new algorithms are being analyzed and researched upon using various convolutional architectures to make the algorithm as efficient and truthful as possible. The crucial attention of researchers during the current COVID-19 situation is to devise means to manage this problem through quick and efficient solutions. These convolutional architectures have made it possible to bring out even the pixel details. The objective is to design a face classifier that can spot any face present in the frame regardless of its alignment, detect the unmasked facial regions, and enhance the recognition accuracy of different masked faces.
COVID-19, Masked Face, Artificial Intelligence, CNN, Convolution Neural Network
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