An Efficient Masked Face Classifier Using EfficientNet

The widening spread of an infectious disease, namely COVID-19 (corona virus disease 2019) has become a serious health problem for countries around the world. As an effort to control the confirmed case of COVID-19, WHO advice people to wear a face mask to protect themselves from the spread of the coronavirus. However, the people's awareness of wearing face masks in certain location is still low, so an automatic masked face classifier model is needed. This research performes the masked face classification into two class (masked and not-masked) by using the EfficientNet pretrained model. The experiment show that the EfficientNet is able to achieve, not only the best accuracy, but also the most efficient model, compared to the other state-of-the-art model of CNN, such as ResNet-50 and Inception. This condition is achieved when using both limited dataset and larger dataset. EfficientNet is able to achieve the 99% accuracy with about 5x lower number of network parameters and 3x faster testing time than ResNet-50. Compare to Inception, EfficientNet is better in terms of accuracy and also efficiency.