  Cascade classifiers are widely used in real-time object detection. Different
from conventional classifiers that are designed for a low overall
classification error rate, a classifier in each node of the cascade is required
to achieve an extremely high detection rate and moderate false positive rate.
Although there are a few reported methods addressing this requirement in the
context of object detection, there is no a principled feature selection method
that explicitly takes into account this asymmetric node learning objective. We
provide such an algorithm here. We show a special case of the biased minimax
probability machine has the same formulation as the linear asymmetric
classifier (LAC) of \cite{wu2005linear}. We then design a new boosting
algorithm that directly optimizes the cost function of LAC. The resulting
totally-corrective boosting algorithm is implemented by the column generation
technique in convex optimization. Experimental results on object detection
verify the effectiveness of the proposed boosting algorithm as a node
classifier in cascade object detection, and show performance better than that
of the current state-of-the-art.
