[Robot Perception and Learning] PAL lab meeting 27, April, 2006 (Casey) Robutst Real-Time Face Detection

Authors: Paul Viola, Michael Jones
From: International Journal of Computer Vision 2004
Abstract:
This paper describes a face 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 simple and efficient classifier which is built using the AdaBoost learning algorithm
(Freund and Schapire, 1995) to select a small number of critical visual features from a very large set of
potential features. 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 face-like
regions. A set of experiments in the domain of face detection is presented. The system yields face detection performance
comparable to the best previous systems (Sung and Poggio, 1998; Rowley et al., 1998; Schneiderman and
Kanade, 2000; Roth et al., 2000). Implemented on a conventional desktop, face detection proceeds at 15 frames per
second.

Paper link: http://www.vision.caltech.edu/html-files/EE148-2005-Spring/pprs/viola04ijcv.pdf