Given pictures with multiple features such as faces, can a single AI algorithm detect all of them, or for better reliability is it preferred to use separate instances?
In other words, I'm talking about an attempt of finding all possible human faces in the same picture by a single neural network.
AFAIK, normally detection algorithms work in a sub-window of the image and not the whole of it. For example, for a specific size and orientation you slide a sub-window on the image and extract sub-images. Then you apply your algorithm on every sub-image for detection and report the size-and-orientations with positive results.
You can have a single neural network for face detection in this case or you might want to have different detectors for different orientation or any other feature, that is your decision.
There is also the technique of Combining Classifiers by which you can improve the decision of single classifiers by combining them.
Ensemble Learning is another way in which your classifiers are not trained independently but rather together. In fact, the well-known object detector of Viola and Jones uses such a technique.
