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Object detection is one of the key tasks in computer vision. The cascadeframework of Viola and Jones has become the de facto standard. A classifier ineach node of the cascade is required to achieve extremely high detection rates,instead of low overall classification error. Although there are a few reportedmethods addressing this requirement in the context of object detection, thereis no a principled feature selection method that explicitly takes into accountthis asymmetric node learning objective. We provide such a boosting algorithmin this work. It is inspired by the linear asymmetric classifier (LAC) of Wu etal. in that our boosting algorithm optimizes a similar cost function. The newtotally-corrective boosting algorithm is implemented by the column generationtechnique in convex optimization. Experimental results on face detectionsuggest that our proposed boosting algorithms can improve the state-of-the-artmethods in detection performance.

LACBoost and FisherBoost: Optimally Building Cascade Classifiers