Author Description

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.