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  • 11School of Computer Science, University of Nottingham, Nottingham, UK.

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Summary
This summary is machine-generated.

This study introduces a new Extended Project-Out Inverse Compositional (E-POIC) algorithm for Active Appearance Models (AAMs). E-POIC offers efficient and robust AAM fitting, improving generalization to unseen variations in images.

Keywords:
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Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Analysis

Background:

  • Active Appearance Models (AAMs) fitting algorithms face a trade-off between robustness and speed.
  • Existing methods struggle to generalize well to unseen variations in facial images.

Purpose of the Study:

  • To develop novel AAM fitting algorithms that are both efficient and robust.
  • To unify and revise existing AAM optimization problems and solutions.
  • To improve the generalization capabilities of AAMs for unconstrained image scenarios.

Main Methods:

  • Introduced a "project-out" optimization framework to unify AAM fitting algorithms.
  • Developed robust simultaneous AAM fitting algorithms with manageable complexity.
  • Proposed the Extended Project-Out Inverse Compositional (E-POIC) algorithm for efficient and robust fitting.
  • Implemented a part-based AAM with a translational motion model.
  • Trained AAMs using SIFT descriptors on "in-the-wild" datasets.

Main Results:

  • The E-POIC algorithm demonstrated superior efficiency and robustness compared to current methods.
  • Part-based AAMs showed improved fitting and convergence properties.
  • AAMs trained with SIFT descriptors performed well on unseen, unconstrained images.
  • The proposed methods significantly narrowed the gap between exact and approximate AAM fitting techniques.
  • Achieved performance comparable to state-of-the-art face alignment systems.

Conclusions:

  • The developed AAM fitting algorithms, particularly E-POIC, offer a significant advancement in balancing speed and robustness.
  • The part-based approach and "in-the-wild" training enhance AAMs' applicability to real-world, diverse datasets.
  • These contributions provide a more effective solution for face alignment tasks.