Abstract

We present a joint face alignment technique that takes a set of images as input and produces a set of shape- and appearance- consistent face alignments as output. Our method is an extension of the recent localization method of Belhumeur et al. [1], which combines the output of local detectors with a non-parametric set of face shape models. We are inspired by the recent joint alignment method of Zhao et al. [20], which employs a modified Active Appearance Model (AAM) approach to align a batch of images. We introduce an approach for simultaneously optimizing both a local appearance constraint, which couples the local estimates between multiple images, and a global shape constraint, which couples landmarks and images across the image set. In video sequences, our method greatly improves the temporal stability of landmark esti- mates without compromising accuracy relative to ground truth.