  Object detection is one of the key tasks in computer vision. The cascade
framework of Viola and Jones has become the de facto standard. A classifier in
each node of the cascade is required to achieve extremely high detection rates,
instead of low overall classification error. 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 a boosting algorithm
in this work. It is inspired by the linear asymmetric classifier (LAC) of Wu et
al. in that our boosting algorithm optimizes a similar cost function. The new
totally-corrective boosting algorithm is implemented by the column generation
technique in convex optimization. Experimental results on face detection
suggest that our proposed boosting algorithms can improve the state-of-the-art
methods in detection performance.
