  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 principled feature selection method
that explicitly takes into account this asymmetric node learning objective. We
provide such an algorithm here. We show that a special case of the biased
minimax probability machine has the same formulation as the linear asymmetric
classifier (LAC) of Wu et al (2005). 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.
