Active Orientation Models for Face Alignment In-the-Wild

Georgios Tzimiropoulos, Joan Alabort-i-Medina, Stefanos Zafeiriou, Maja Pantic

    Research output: Contribution to journalArticleAcademicpeer-review

    14 Citations (Scopus)

    Abstract

    We present Active Orientation Models (AOMs), generative models of facial shape and appearance, which extend the well-known 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 Principal Component Analysis 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 the 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: 1) AOMs generalize well to unseen variations and 2) 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.
    Original languageUndefined
    Pages (from-to)2024-2034
    Number of pages11
    JournalIEEE transactions on information forensics and security
    Volume9
    Issue number12
    DOIs
    Publication statusPublished - Dec 2014

    Keywords

    • EWI-25805
    • HMI-HF: Human Factors
    • EC Grant Agreement nr.: FP7/611153
    • EC Grant Agreement nr.: FP7/2007-2013
    • METIS-309938
    • Active orientation models
    • Face alignment
    • IR-95239
    • Active Appearance Models

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