For people who just start thinking about computer vision, it is of hard to understand why computers have such a difficult time finding faces in images, even though it is so easy for us.
Adding to one of my earlier articles about why is vision hard, which points out, that computers are missing concepts, there is another reason. They are also missing context. It is so easy for us, to spot faces, because we know were to look for them, where to expect them. When we see a person, we know were to look for the face, and when we see a house, we know, that we wont find a face. But computers don’t. And to show you that even we are lost without context I present you this nice picture with the coffee beans. Your job is to find the face.
Did you find the face? Probably you didn’t find it at once, but started to scan the picture till you found it. It took me nearly 30s,which is so much slower, than any recent software.
So how can we improve our algorithm with context?
About the picture. I got it from a very interesting talk held at TID by Aleix M Martinez from the Ohio State University on classification. His main point, PCA and LDA. For starters check out his paper PCA versus LDA (2001).