We present a novel method for efficiently training a face detector using large positive
datasets in a cascade of boosted ensembles. We extend the successful Viola-Jones [1] framework
which achieved low false acceptance rates through bootstrapping negative samples with the
capability to also bootstrap large positive datasets thereby capturing more in-class variation
of the target object. We achieve this form of bootstrapping by way of an additional embedded
cascade within each layer and term the new structure as the Bootstrapped Dual-Cascaded
(BDC) framework. We demonstrate its ability to easily and efficiently train a classifier on
large and complex face datasets which exhibit acute in-class variation.
