So much software related to computer vision relies on AI and neural networks, I wonder some of the approaches which don't use those methods. How could some of the mainstays of computer vision (eg. recognition and identification) be achieved without "artificial" means?
I'm reading Vision by David Marr and it talks about the human visual system model and how it could be emulated algorithmically for computer vision. I know of salience mapping and edge detection algorithms which there's significant consensus that the brain processes visual information in a manner not too dissimilar to, but what other methods are there?
Consider something such as facial recognition: I have a headshot of hundreds of individuals, how could a system recognize if a new face was novel or previously recognized without neural networks or deep learning? My interest is in white-box approaches to computer vision rather than these black-box approaches.