Abstract

Ob ject 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 ob ject detection, there is no a principled feature se- lection method that explicitly takes into account this asymmetric node learning ob jective. We provide such a boosting algorithm in this work. It is inspired by the linear asymmetric classifier (LAC) of [1] 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.