Boosting is based on the following question: “Can a set of weak learners create a single strong learner?” A weak learner (or weak classifier) is defined as a classifier that is only slightly better than random guessing.
In face detection, this means that a weak learner can classify a subregion of an image as a face or not-face only slightly better than random guessing. A strong learner is substantially better at picking faces from non-faces.
The power of boosting comes from combining many (thousands) of weak classifiers into a single strong classifier. In the Viola-Jones algorithm, each Haar-like feature represents a weak learner. To decide the type and size of a feature that goes into the final classifier, AdaBoost checks the performance of all classifiers that you supply to it.
To calculate the performance of a classifier, you evaluate it on all subregions of all the images used for training. Some subregions will produce a strong response in the classifier. Those will be classified as positives, meaning the classifier thinks it contains a human face.
Subregions that don’t produce a strong response don’t contain a human face, in the classifiers opinion. They will be classified as negatives.
The classifiers that performed well are given higher importance or weight. The final result is a strong classifier, also called a boosted classifier, that contains the best performing weak classifiers.
The algorithm is called adaptive because, as training progresses, it gives more emphasis on those images that were incorrectly classified. The weak classifiers that perform better on these hard examples are weighted more strongly than others.