The answer is, sometimes. :) It all depends on what data you trained with, the variety of light/shape/tilt of the faces, etc. The code can only build on what it

The answer is, sometimes. :) It all depends on what data you trained with, the variety of light/shape/tilt of the faces, etc. The code can only build on what it knows, so when it fails the best thing to do is dog-food that case and add it to the training set. Of course, outliers like that can also cause more false positives as the decision function has to generalize even more to cover those cases.