  Wearing a mask is a strong protection against the COVID-19 pandemic, even
though the vaccine has been successfully developed and is widely available.
However, many people wear them incorrectly. This observation prompts us to
devise an automated approach to monitor the condition of people wearing masks.
Unlike previous studies, our work goes beyond mask detection; it focuses on
generating a personalized demonstration on proper mask-wearing, which helps
people use masks better through visual demonstration rather than text
explanation. The pipeline starts from the detection of face covering. For
images where faces are improperly covered, our mask overlay module incorporates
statistical shape analysis (SSA) and dense landmark alignment to approximate
the geometry of a face and generates corresponding face-covering examples. Our
results show that the proposed system successfully identifies images with faces
covered properly. Our ablation study on mask overlay suggests that the SSA
model helps to address variations in face shapes, orientations, and scales. The
final face-covering examples, especially half profile face images, surpass
previous arts by a noticeable margin.
