Description: 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 [4]. 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 performance comparable to
the best previous systems [16, 11, 14, 10, 1]. Implemented on a conventional desktop,
face detection proceeds at 15 frames per second
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