On the Design of Cascades of Boosted Ensembles for Face Detection

Cascades of boosted ensembles have become popular in the object detection community following their highly successful introduction in the face detector of Viola and Jones. Since then, researchers have sought to improve upon the original approach by incorporating new methods along a variety of axes (e.g. alternative boosting methods, feature sets, etc). We explore several axes that have not yet received adequate attention in this context: cascade learning, stronger weak hypotheses, and feature filtering. We present a novel strategy to determine the appropriate balance between false positive and detection rates in the individual stages of the cascade, enabling us to control our experiments to a degree not previously possible. We show that while the choice of boosting method has little impact on the detector's performance and feature filtering is largely ineffective, the use of stronger weak hypotheses based on CART classifiers can significantly improve upon the standard results