Robust Real-Time Face Detection View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2004-05

AUTHORS

Paul Viola, Michael J. Jones

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. More... »

PAGES

137-154

References to SciGraph publications

  • 2001-01. Coarse-to-Fine Face Detection in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 1986-03. Induction of decision trees in MACHINE LEARNING
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    N-Triples is a line-based linked data format ideal for batch operations.

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    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1023/b:visi.0000013087.49260.fb'

    RDF/XML is a standard XML format for linked data.

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