CiteWeb id: 20040000010

CiteWeb score: 10605

DOI: 10.1023/B:VISI.0000013087.49260.fb

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.

The publication "Robust Real-Time Face Detection" is placed in the Top 1000 of the best publications in CiteWeb. Also in the category Computer Science it is included to the Top 100. Additionally, the publicaiton "Robust Real-Time Face Detection" is placed in the Top 100 among other scientific works published in 2004.
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