Robust Real-time Object Detection<p><a href="http://research.microsoft.com/en-us/um/people/viola/Pubs/Detect/violaJones_IJCV.pdf" rel="nofollow">http://research.microsoft.com/en-us/um/people/viola/Pubs/Det...</a><p>This paper describes a visual object detection framework that is capable of processing images extremely
rapidly while achieving high detection rates. There are three key contributions. The first is the introduction
of a new image representation called the “Integral Image” which allows the features used by our detector
to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small
number of critical visual features and yields extremely efficient classifiers [6]. The third contribution is a
method for combining classifiers in a “cascade” which allows background regions of the image to be quickly
discarded while spending more computation on promising object-like regions. A set of experiments in the
domain of face detection are presented. The system yields face detection performace comparable to the best
previous systems [18, 13, 16, 12, 1]. Implemented on a conventional desktop, face detection proceeds at 15
frames per second.