[jonjlee]

This is incredible. It seems to perform well on faces with dysmorphic features including cleft lips, asymmetric eyes, and abnormal locations of facial landmarks. It even creates an appropriate 3D representation of a cleft. I've found that some other face detection implementations actually fail at even localizing faces with these abnormalities. I don't have the background to fully understand the paper, though. Is there anything truly novel in how they are detecting faces? Could their work be leveraged to more accurately label facial landmarks for "abnormal" faces?

[aaronsjackson]

I'm really happy to hear that it works on those sorts of cases. I've tried a few images during testing, such as unusually long noses, and was pleased with how they came out. The novelty comes from a simple approach to a usually quite complex problem (i.e. posing the problem as a semantic segmentation problem, to produce a spatially aligned volume)