Let’s start with an example. Suppose we are charged with providing automated access control to a building. Before entering the building each person has to look into a camera so we can take a still image of their face. For our purposes it suffices just to decide based on the image whether the person can enter the building. It might be helpful to (try to) also identify each person but this might require type of information we do not have (e.g., names or whether any two face images correspond to the same person). We only have face images of people recorded while access control was still provided manually. As a result of this experience we have labeled images. An image is labeled positive if the person in question should gain entry and negative otherwise. To supplement the set of negatively labeled images (as we would expect only few cases of refused entries under normal circumstances) we can use any other face images of people who we do not expect to be permitted to enter the building. Images taken with similar camera-face orientation (e.g., from systems operational in other buildings) would be preferred. Our task then is to come up with a function – a classifier – that maps pixel images to binary (±1) labels. And we only have the small set of labeled images (the training set) to constrain the function.
