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Anchored Deformable Face Ensemble Alignment
Xin Cheng1, Sridha Sridharan1, Jason Saraghi2, and Simon Lucey1, 2
1Queensland University of Technology, Australia
x2.cheng@qut.edu.au
s.sridharan@qut.edu.au
simon.lucey@csiro.au
2The Commonwealth Scientific and Industrial Research Organisation, Australia
jason.saraghi@csiro.au
Abstract. At present, many approaches have been proposed for deformable face alignment with varying degrees of success. However, the common drawback to nearly all these approaches is the inaccurate landmark registrations. The registration errors which occur are predominantly heterogeneous (i.e. low error for some frames in a sequence and higher error for others). In this paper we propose an approach for simultaneously aligning an ensemble of deformable face images stemming from the same subject given noisy heterogeneous landmark estimates. We propose that these initial noisy landmark estimates can be used as an “anchor” in conjunction with known state-of-the-art objectives for unsupervised image ensemble alignment. Impressive alignment performance is obtained using well known deformable face fitting algorithms as “anchors”.
LNCS 7583, p. 133 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012