Some of them do, but others depend on labeled and augmented data. For example training facial recognition starts with images labeled by who appears in them, then you run a model to find the face in the image and crop to that, then another image tells you where the eyes are and you use that to rotate the image to make the eyes level, and that's the input to the actual facial recognition neural network. And that's for the "naive" approaches that don't have special handling for faces don't look at the camera. And the field still routinely has problems because it turns out that somebody's data set only contained certain lighting conditions, or severely underrepresented certain races, etc.