  Cascaded regression is prevailing in face alignment thanks to its accuracy
and robustness, but typically demands manually annotated examples having low
discrepancy between shape-indexed features and shape updates. In this paper, we
propose a self-reinforced strategy that iteratively expands the quantity and
improves the quality of training examples, thus upgrading the performance of
cascaded regression itself. The reinforced term evaluates the example quality
upon the consistence on both local appearance and global geometry of human
faces, and constitutes the example evolution by the philosophy of "survival of
the fittest". We train a set of discriminative classifiers, each associated
with one landmark label, to prune those examples with inconsistent local
appearance, and further validate the geometric relationship among groups of
labeled landmarks against the common global geometry derived from a projective
invariant. We embed this generic strategy into typical cascaded regressions,
and the alignment results on several benchmark data sets demonstrate its
effectiveness to predict good examples starting from a small subset.
