A note on versions: The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. For more information, please contact eprints@nottingham.ac.uk 1556-6013 (c) Abstract-We present Active Orientation Models, generative models of facial shape and

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... e, which extend the wellknown paradigm of Active Appearance Models (AAMs) for the case of generic face alignment under unconstrained conditions. Robustness stems from the fact that the proposed AOMs employ a statistically robust appearance model based on the principal components of image gradient orientations. We show that when incorporated within standard optimization frameworks for AAM learning and fitting, this kernel PCA results in robust algorithms for model fitting. At the same time, the resulting optimization problems maintain the same computational cost. As a result the main similarity of AOMs with AAMs is computational complexity. In particular, the project-out version of AOMs is as computationally efficient as the standard project-out inverse compositional algorithm which is admittedly one of the fastest algorithms for fitting AAMs. We verify experimentally that (i) AOMs generalize well to unseen variations and (ii) outperform all other state-of-the-art AAM methods considered by a large margin. This performance improvement brings AOMs at least in par with other contemporary methods for face alignment. Finally, we provide Matlab code at http://ibug.doc.ic.ac.uk/resources.