Masked Face Recognition Using FaceNet

The global epidemic of COVID-19 has seriously affected people's life. To prevent and control the outbreak, people are required to wear masks, which poses a formidable challenge to the existing face recognition system. A masked face recognition method based on FaceNet is proposed to tackle the problems. In this paper, a smaller model based on the Inception-ResN et Vl model is proposed. The main idea is to reduce filter numbers in each inception block while maintaining the whole structure. The reduced version has much fewer parameters to compute and can recognize faces with and without masks. Comprehensive experiments on both masked and unmasked datasets have been conducted. With 99.79% test accuracy in the masked MS-Celeb-1M dataset, the model trained in this paper can be integrated into existing face recognition programs designed to recognize faces for verification purposes.