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Robust Real-Time Face Detection
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图像处理的外文参考文献Robust Real-Time Face Detection
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Robust Real-Time Face Detection
PAUL VIOLA
Microsoft Research, One Microsoft Way, Redmond, WA 98052, USA viola@microsoft.com
MICHAEL J. JONES
Mitsubishi Electric Research Laboratory, 201 Broadway, Cambridge, MA 02139, USA
mjones@merl.com
Received September 10, 2001; Revised July 10, 2003; Accepted July 11, 2003
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 algo-rithm (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 back-ground
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 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 per second.
This paper brings together new algorithms and insights to construct a framework for robust and
extremely rapid visual detection. Toward this end we have constructed a frontal face detection
system which achieves detec-tion and false positive rates which are equivalent to the best
published results (Sung and Poggio, 1998;Rowleyetal.,1998;Osunaetal.,1997a;Schneiderman and
Kanade, 2000; Roth et al., 2000). This face detec-tion system is most clearly distinguished from
previ-ous approaches in its ability to detect faces extremely rapidly. Operating on 384 by 288 pixel
images, faces are detected at 15 frames per second on a conventional 700 MHz Intel Pentium III.
In other face detection systems, auxiliary information, such as image differ- ences in video
sequences, or pixel color in color im-ages, have been used to achieve high frame rates. Our system
achieves high frame rates working only with the information present in a single grey scale image.
These alternative sources of information can also be integrated with our system to achieve even
higher frame rates.
There are three main contributions of our face detec-tion framework. We will introduce each
of these ideas briefly below and then describe them in detail in sub-sequent sections.
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