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2D facial landmark localization method for multi-view face synthesis image using a two-pathway generative adversarial

Mahmood H B Alhlffee1, Yea-Shuan Huang2, Yi-An Chen2

  • 1College of Computer Science and Electrical Engineering, Chung-Hua University, Hsinchu, Taiwan.

Peerj. Computer Science
|May 2, 2022
PubMed
Summary

This study introduces a landmark feature-based method (LFM) to enhance multi-view face synthesis for robust facial recognition. The novel approach improves the quality of generated frontal faces, especially under extreme poses, outperforming existing generative adversarial network (GAN) methods.

Keywords:
Face synthesisGenerative adversarial networkLandmark detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Multi-view face synthesis from single images is crucial for facial recognition but challenging under extreme poses.
  • Existing generative adversarial network (GAN) methods, including TP-GAN, struggle with high-quality frontal face generation in extreme pose scenarios.
  • TP-GAN's reliance on texture details for synthesis limits its performance in extreme pose conditions.

Purpose of the Study:

  • To propose a novel landmark feature-based method (LFM) for robust pose-invariant facial recognition.
  • To enhance the image resolution quality of generated frontal faces across various facial poses.
  • To improve the performance of generative adversarial networks in synthesizing high-quality frontal face images from single images under extreme poses.

Main Methods:

  • Augmenting the TP-GAN framework's global pathway with 2D face landmark localization.
  • Implementing a landmark sharing mechanism between global and local pathways.
  • Enhancing the encoder-decoder global pathway structure with robust feature extractors for better facial image representation.
  • Developing a balanced learning strategy for improved feature selection and operational workflow.

Main Results:

  • The proposed LFM significantly improves the photorealistic face image resolution.
  • Generated frontal faces exhibit high perceptual quality even under extreme poses.
  • Experimental results on Multi-PIE and FEI datasets demonstrate superior performance compared to the original TP-GAN method.

Conclusions:

  • The LFM effectively addresses the limitations of existing methods in generating high-quality frontal faces from extreme poses.
  • The integration of landmark features enhances pose-invariant facial recognition capabilities.
  • The method offers a promising advancement for robust facial recognition systems in diverse and challenging conditions.