Active orientation models for face alignment in-the-wild

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

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.

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), https://doi.org/10.1109/TIFS.2014.2361018

• Journal Article Type: Article
• Publication Date: Nov 11, 2014
• Deposit Date: Jan 29, 2016
• Publicly Available Date: Jan 29, 2016
• Journal: IEEE Transactions on Information Forensics and Security
• Print ISSN: 1556-6013
• Electronic ISSN: 1556-6021
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Issue: 12
• DOI: https://doi.org/10.1109/TIFS.2014.2361018
• Keywords: Computational Complexity, Face Recognition, Optimisation, Principal Component Analysis
• Public URL: https://nottingham-repository.worktribe.com/output/739788
• Publisher URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6914605
• 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.