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Active orientation models for face alignment in-the-wild

Tzimiropoulos, Georgios; Medina, Joan Alabort; Zafeiriou, Stefanos; Pantic, Maja

Authors

Georgios Tzimiropoulos

Joan Alabort Medina

Stefanos Zafeiriou

Maja Pantic



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.

Journal Article Type Article
Publication Date Nov 11, 2014
Journal IEEE Transactions on Information Forensics and Security
Print ISSN 1556-6013
Electronic ISSN 1556-6013
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 9
Issue 12
APA6 Citation Tzimiropoulos, G., Medina, J. A., Zafeiriou, S., & Pantic, M. (2014). Active orientation models for face alignment in-the-wild. IEEE Transactions on Information Forensics and Security, 9(12), doi:10.1109/TIFS.2014.2361018
DOI https://doi.org/10.1109/TIFS.2014.2361018
Keywords Computational Complexity, Face Recognition, Optimisation, Principal Component Analysis
Publisher URL http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6914605
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information ©2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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