As mentioned earlier, face recognition is extremely vulnerable to changes in lighting conditions, face orientation, face expression, and so on, so it is very important to reduce these differences as much as possible. Otherwise, the face recognition algorithm will often think there is more similarity between the faces of two different people in the same conditions, than between two images of the same person.
The easiest form of face preprocessing is just to apply histogram equalization using the equalizeHist() function, like we just did for face detection. This may be sufficient for some projects where the lighting and positional conditions won't change by much. But for reliability in real-world conditions, we need many sophisticated techniques, including facial feature detection (for example, detecting eyes, nose, mouth, and eyebrows). For simplicity, this chapter will just use eye detection and ignore other facial features such as the mouth and nose, which are less useful.
The following photo shows an enlarged view of a typical preprocessed face, using the techniques that will be covered in this section:
