Proficient Masked Face Recognition Method Using Deep Learning Convolution Neural Network in Covid-19 Pandemic

Abstract

The COVID-19 pandemic is an incomparable disaster triggering massive fatalities and security glitches. Under the pressure of these black clouds public frequently wear masks as safeguard to their lives. Facial Recognition becomes a challenge because significant portion of human face is hidden behind mask. Primarily researchers focus to derive up with recommendations to tackle this problem through prompt and effective solution in this COVID-19 pandemic. This paper presents a trustworthy method to for the recognition of masked faces on un-occluded and deep learning-based features. The first stage is to capture the non-obstructed face region. Then we extract the most significant features from the attained regions (forehead and eye) through pre-trained deep learning CNN. Bag-of- word paradigm to has been applied to the feature maps to quantize them and to get a minor illustration comparing to the CNN’s fully connected layer. In the end a Multilayer Perceptron has been used for classification. High recognition performance with significant accuracy is seen in experimental results.