In recent years, the COVID-19 pandemic has negatively impacted all aspects of social life. Due to ease in the infected method, i.e., through small liquid particles from the mouth or the nose when people cough, sneeze, speak, sing, or breathe, the virus can quickly spread and create severe problems for people’s health. According to some research as well as World Health Organization (WHO) recommendation, one of the most economical and effective methods to prevent the spread of the pandemic is to ask people to wear the face mask in the public space. A face mask will help prevent the droplet and aerosol from person to person to reduce the risk of virus infection. This simple method can reduce up to 95% of the spread of the particles. However, this solution depends heavily on social consciousness, which is sometimes unstable. In order to improve the effectiveness of wearing face masks in public spaces, this research proposes an approach for detecting and warning a person who does not wear or misuse the face mask. The approach uses the deep learning technique that relies on GoogleNet, AlexNet, and VGG16 models. The results are synthesized by an ensemble method, i.e., the bagging technique. From the experimental results, the approach represents a more than 95% accuracy of face mask recognition.

Face mask detection; deep learning; AlexNet; GoogLeNet; VGG16; ensemble method