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Fast algorithms for fitting active appearance models to unconstrained images

Tzimiropoulos, Georgios; Pantic, Maja

Fast algorithms for fitting active appearance models to unconstrained images Thumbnail


Authors

Georgios Tzimiropoulos

Maja Pantic



Abstract

Fitting algorithms for Active Appearance Models (AAMs) are usually considered to be robust but slow or fast but less able to generalize well to unseen variations. In this paper, we look into AAM fitting algorithms and make the following orthogonal contributions: We present a simple “project-out” optimization framework that unifies and revises the most well-known optimization problems and solutions in AAMs. Based on this framework, we describe robust simultaneous AAM fitting algorithms the complexity of which is not prohibitive for current systems. We then go on one step further and propose a new approximate project-out AAM fitting algorithm which we coin extended project-out inverse compositional (E-POIC). In contrast to current algorithms, E-POIC is both efficient and robust. Next, we describe a part-based AAM employing a translational motion model, which results in superior fitting and convergence properties. We also show that the proposed AAMs, when trained “in-the-wild” using SIFT descriptors, perform surprisingly well even for the case of unseen unconstrained images. Via a number of experiments on unconstrained human and animal face databases, we show that our combined contributions largely bridge the gap between exact and current approximate methods for AAM fitting and perform comparably with state-of-the-art face alignment algorithms.

Journal Article Type Article
Acceptance Date Sep 3, 2016
Online Publication Date Sep 24, 2016
Deposit Date Sep 16, 2016
Publicly Available Date Sep 24, 2016
Journal International Journal of Computer Vision
Print ISSN 0920-5691
Electronic ISSN 1573-1405
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1007/s11263-016-0950-1
Keywords Active appearance models, Face alignment, In-the-wild
Public URL https://nottingham-repository.worktribe.com/output/809574
Publisher URL http://link.springer.com/article/10.1007%2Fs11263-016-0950-1

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