Camera-Based Security Check for Face Mask Detection using Deep Learning
Proceedings - IEEE 7th International Conference on Big Data Computing Service and Applications, BigDataService 2021
According to the World Health Organization (WHO), wearing a face mask is one of the most effective protections from airborne infectious diseases such as COVID-19. Since the spread of COVID-19, infected countries have been enforcing strict mask regulation for indoor businesses and public spaces. While wearing a mask is a requirement, the position and type of the mask should also be considered in order to increase the effectiveness of face masks, especially at specific public locations. However, this makes it difficult for conventional facial recognition technology to identify individuals for security checks. To solve this problem, Deep Learning algorithms are proposed to develop a comprehensive mask detection through a camera. CNN, AlexNet, VGG16, and Facial Recognition Pipeline with FaceNet are the Deep Learning algorithms that are used to classify the features in each scenario. This project uses multiple open datasets, Google Image Search, and simulated photos. We aim to build a comprehensive camera system that is able to cover four major issues: Mask Detection, Mask Type Classification, Mask Position Classification and Identity Recognition.
COVID - 19, Deep Learning, Facial Recognition, Machine Learning, Mask Detection
Applied Data Science; Computer Engineering
Ziwei Song, Kristie Nguyen, Tien Nguyen, Catherine Cho, and Jerry Gao. "Camera-Based Security Check for Face Mask Detection using Deep Learning" Proceedings - IEEE 7th International Conference on Big Data Computing Service and Applications, BigDataService 2021 (2021): 96-106. https://doi.org/10.1109/BigDataService52369.2021.00017