MLFW: A Database for Face Recognition on Masked Faces
This addresses a practical issue for face recognition systems in real-world scenarios where masks are common, but it is incremental as it primarily involves generating a new dataset from existing data.
The authors tackled the problem of face recognition performance degradation due to masks during the COVID-19 pandemic by creating a new database called Masked LFW (MLFW) from existing face images. They found that state-of-the-art models' recognition accuracy declined by 5%-16% on this database compared to original images.
As more and more people begin to wear masks due to current COVID-19 pandemic, existing face recognition systems may encounter severe performance degradation when recognizing masked faces. To figure out the impact of masks on face recognition model, we build a simple but effective tool to generate masked faces from unmasked faces automatically, and construct a new database called Masked LFW (MLFW) based on Cross-Age LFW (CALFW) database. The mask on the masked face generated by our method has good visual consistency with the original face. Moreover, we collect various mask templates, covering most of the common styles appeared in the daily life, to achieve diverse generation effects. Considering realistic scenarios, we design three kinds of combinations of face pairs. The recognition accuracy of SOTA models declines 5%-16% on MLFW database compared with the accuracy on the original images. MLFW database can be viewed and downloaded at \url{http://whdeng.cn/mlfw}.