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As more and more people begin to wear masks due to current COVID-19 pandemic,existing face recognition systems may encounter severe performance degradationwhen recognizing masked faces. To figure out the impact of masks on facerecognition model, we build a simple but effective tool to generate maskedfaces from unmasked faces automatically, and construct a new database calledMasked LFW (MLFW) based on Cross-Age LFW (CALFW) database. The mask on themasked face generated by our method has good visual consistency with theoriginal face. Moreover, we collect various mask templates, covering most ofthe common styles appeared in the daily life, to achieve diverse generationeffects. Considering realistic scenarios, we design three kinds of combinationsof face pairs. The recognition accuracy of SOTA models declines 5%-16% on MLFWdatabase compared with the accuracy on the original images. MLFW database canbe viewed and downloaded at \url{http://whdeng.cn/mlfw}.

MLFW: A Database for Face Recognition on Masked Faces