Some face recognition algorithms, such as OpenCV's face detection algorithm make heavy use of features like the ones shown in the following image:
These are the so-called Haar-like features and they are calculated as the sum of pixels in the white area minus the sum of pixels in the black area. You might find this type of a feature kind of odd, but when training it for face detection, it can be built to be an extremely powerful classifier using only two of these features, as depicted in the following image:
In fact, a classifier that uses only the two preceding features can be adjusted to detect 100 percent of a given face training database with only 40 percent of false positives. Taking out the sum of all pixels in an image as well as calculating the sum of each area can be a long process. However, this process must be tested for each frame in a given input image, hence calculating these features fast is a requirement that we need to fulfill.
