  Real-time object detection is one of the core problems in computer vision.
The cascade boosting framework proposed by Viola and Jones has become the
standard for this problem. In this framework, the learning goal for each node
is asymmetric, which is required to achieve a high detection rate and a
moderate false positive rate. We develop new boosting algorithms to address
this asymmetric learning problem. We show that our methods explicitly optimize
asymmetric loss objectives in a totally corrective fashion. The methods are
totally corrective in the sense that the coefficients of all selected weak
classifiers are updated at each iteration. In contract, conventional boosting
like AdaBoost is stage-wise in that only the current weak classifier's
coefficient is updated. At the heart of the totally corrective boosting is the
column generation technique. Experiments on face detection show that our
methods outperform the state-of-the-art asymmetric boosting methods.
