Fast Algorithms for Fitting Active Appearance Models to Unconstrained Images

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• 作者：Georgios Tzimiropoulos ; Maja Pantic
• 关键词：Active Appearance Models ; Face alignment ; In ; the ; wild
• 刊名：International Journal of Computer Vision
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：122
• 期：1
• 页码：17-33
• 全文大小：3725KB
• 刊物类别：Computer Science
• 刊物主题：Computer Imaging, Vision, Pattern Recognition and Graphics; Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision; Pattern Recognition;
• 出版者：Springer US
• ISSN：1573-1405
• 卷排序：122

文摘

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 systems.