[Reading] Rapid Object Detection using a Boosted Cascade of Simple Features

This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. It uses haar features for weak learners, by using the "integral image"
technique, those features can be computed very very quickly. It uses adaboost as learning algorithm. It selects a small number of most important features from a larger set and yields extremely efficient, discriminative classifiers. It propose a "cascade" framework for providing efficiently distiguishing between face and nonface. Overall, this paper propose an approach for object detection which minimizes computation time while achieving high detection accuracy.