LACBoost and FisherBoost: Optimally Building Cascade Classifiers

TL;DR

This paper introduces LACBoost and FisherBoost, two boosting algorithms designed to optimize cascade classifiers for object detection, explicitly addressing the asymmetric detection requirements and improving detection performance.

Contribution

It presents a novel, principled feature selection method for cascade classifiers that explicitly considers the asymmetric node learning objective, inspired by LAC and implemented via convex optimization.

Findings

Improved face detection performance over state-of-the-art methods

Effective feature selection tailored for cascade classifiers

Demonstrated advantages of the proposed boosting algorithms

Abstract

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.