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Robust Statistical Frontalization of Human and Animal Faces.

Christos Sagonas1, Yannis Panagakis1, Stefanos Zafeiriou1

  • 11Department of Computing, Imperial College London, 180 Queens Gate, London, SW7 2AZ UK.

International Journal of Computer Vision
|April 1, 2020
PubMed
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This study introduces a novel method for robust facial landmark localization and frontalization, effective even with pose variations and occlusions. It requires minimal frontal images, outperforming existing techniques in various facial analysis tasks.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Biometrics

Background:

  • Unconstrained facial data presents challenges like pose variations, illumination changes, and occlusions.
  • Existing facial landmark localization and recognition methods struggle with such variations.
  • State-of-the-art approaches often require extensive manually annotated data or 3D models.

Purpose of the Study:

  • To propose a novel method for joint face frontalization and landmark localization.
  • To develop a technique robust to pose, illumination variations, and occlusions.
  • To reduce reliance on large annotated datasets or 3D models.

Main Methods:

  • A novel optimization approach is devised based on the observation that frontal facial images have minimum rank.
  • Minimization of the nuclear norm (rank surrogate) and matrix norm (for occlusions) is employed.
  • The method jointly recovers frontalized faces and facial landmarks.

Main Results:

  • The method demonstrates effectiveness in frontal view reconstruction for human and animal faces.
  • Successful landmark localization and pose-invariant face recognition were achieved.
  • Strong performance in unconstrained face verification and video inpainting was observed across 9 databases.

Conclusions:

  • The proposed method offers a robust solution for facial analysis under challenging real-world conditions.
  • It significantly outperforms state-of-the-art methods for joint face frontalization and landmark localization.
  • The technique's efficiency is validated across diverse facial analysis applications.