Self-Reinforced Cascaded Regression for Face Alignment

Abstract

Cascaded regression is prevailing in face alignment thanks to its accurate and robust localization of facial landmarks, but typically demands numerous annotated training examples of 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 two typical cascaded regressions, and the alignment results on several benchmark data sets demonstrate the effectiveness of training regressions with automatic example prediction and evolution starting from a small subset.