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International Journal of Computer Vision 57(2), 137–154, 2004c 2004 Kluwer Academic Publishers. Manufactured in The Netherlands.Robust Real-Time Face DetectionPAUL VIOLAMicrosoft Research, One Microsoft Way, Redmond, WA 98052, USAviola@microsoft.comMICHAEL J. JONESMitsubishi Electric Research Laboratory, 201 Broadway, Cambridge, MA 02139, USAmjones@merl.comReceived September 10, 2001; Revised July10, 2003; Accepted July11, 2003Abstract.This paper describes a face detection framework that is capable of processing images extremely rapidlywhile achieving high detection rates. There are three key contributions. The first is the introduction of a newimage representation called the “Integral Image” which allows the features used by our detector to be computedvery quickly. The second is a simple and efficient classifier which is built using the AdaBoost learning algo-rithm (Freund and Schapire, 1995) to select a small number of critical visual features from a very large set ofpotential features. The third contribution is a method for combining classifiers in a “cascade” which allows back-ground regions of the image to be quickly discarded while spending more computation on promising face-likeregions. A set of experiments in the domain of face detection is presented. The system yields face detection perfor-mance comparable to the best previous systems (Sung and Poggio, 1998; Rowley et al., 1998; Schneiderman andKanade, 2000; Roth et al., 2000). Implemented on a conventional desktop, face detection proceeds at 15 frames persecond.Keywords:face detection, boosting, human sensing1.IntroductionThis paper brings together new algorithms and insightsto construct a framework for robust and extremely rapidvisual detection. Toward this end we...