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Asymmetric boosting, while acknowledged to be
important to state-of-the-art face detection, is
typically based on the trial-and-error practice,
rather than on principled methods. This work solves a
number of issues related to asymmetric boosting and
the use of asymmetric boosting in face detection. It
shows how a proper understanding and use of
asymmetric boosting leads to significant improvements
in the
learning time, the learning capacity, the detection
speed and the detection accuracy of a face detector.
There are four main contributions in this book: 1) a
new method to learn online an asymmetric boosted
classifier, pioneering a new direction of online
learning a face detector; 2) a new weak classifier
learning method,
significantly reducing the learning time of a
face detector from weeks to just a few hours; 3) a
new and principled method to learn a
face detector cascade, further improving
the learning time and the detection speed of a face
detector; and 4) a theoretical analysis on the
generalization of an asymmetric boosted classifier
via bounds on the true
asymmetric error of the classifier. The work is
concluded with a discussion of future directions for
face detection.
important to state-of-the-art face detection, is
typically based on the trial-and-error practice,
rather than on principled methods. This work solves a
number of issues related to asymmetric boosting and
the use of asymmetric boosting in face detection. It
shows how a proper understanding and use of
asymmetric boosting leads to significant improvements
in the
learning time, the learning capacity, the detection
speed and the detection accuracy of a face detector.
There are four main contributions in this book: 1) a
new method to learn online an asymmetric boosted
classifier, pioneering a new direction of online
learning a face detector; 2) a new weak classifier
learning method,
significantly reducing the learning time of a
face detector from weeks to just a few hours; 3) a
new and principled method to learn a
face detector cascade, further improving
the learning time and the detection speed of a face
detector; and 4) a theoretical analysis on the
generalization of an asymmetric boosted classifier
via bounds on the true
asymmetric error of the classifier. The work is
concluded with a discussion of future directions for
face detection.