Name
Abstract | ||
|---|---|---|
To reduce the risk of infecting or being infected by the recent COVID-19 virus, wearing mask is enforced or recommended by many countries. AI based system for automatically detecting whether individuals are wearing face mask becomes an urgent requirement in high risk facilities and crowded public places. Due to lacking of existing masked face datasets and the urgent low-cost application requirement, we propose a progressive semi-supervised learning method - called maskedFaceNet to minimize the efforts on data annotation and letting deep models to learn by using less annotated training data. With this method, the detection accuracy is further improved progressively while adapting to various application scenarios. Experimental results show that our maskedFaceNet is more efficient and accurate compared to other methods. Furthermore, we also contribute two masked face datasets for benchmarking and for the benefit of future research. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/WACV48630.2021.00343 | 2021 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION WACV 2021 |
DocType | ISSN | Citations |
Conference | 2472-6737 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shitala Prasad | 1 | 0 | 3.04 |
| Yiqun Li | 2 | 0 | 2.37 |
| Dongyun Lin | 3 | 1 | 3.06 |
| Sheng Dong | 4 | 1 | 2.72 |