Masked face with facial expression recognition based on deep learning

Abstract

Wearing masks contributed to slowing the spread of the coronavirus disease (COVID-19) as the World Health Organization (WHO) recommended wearing face masks especially with the spreading of virus variants like omicron. Although people accept the idea of wearing these masks, it is still unknown the effect of covering parts of the face on social interaction among people in general and children in particular. Moreover, Social isolation affects emotional moods, which causes stress, sadness, and depression. In the current study, we have been exploring the emotional inferences on faces with and without a mask. The system can pick up the universal emotions: fear, disgust, anger, surprise, contempt, sadness, and happiness. The researchers in deep learning are concerned with global pandemic COVID-19 to enhance public health service. The proposed model is developed with a machine learning algorithm through the Haar feature-based cascade classifiers. The built model can detect people's emotions with mask and without a mask with high accuracy.

Sophisticated face mask dataset: a novel dataset for effective coronavirus disease surveillance

“…Utilizing MobileNetV2, Hassan et al [7] developed a real-time mask recognition system on embedded devices with a recognition rate of 99%. In [8], a machine learning model accurately inferred emotions both with and without masks using Haar feature-based cascade classifiers. Hassan et al [9] employed a Jetson Nano, infrared temperature sensor, AMG8833, and C920e camera to achieve 99% and 100% accuracy during training and testing [10] introduced a portable IoT device for COVID-19 guideline enforcement, encompassing mask detection, social distance alerting, crowd analysis, health screening, and assessment.…”