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Facial Landmark-Driven Keypoint Feature Extraction for Robust Facial Expression Recognition.

Jaehyun So1, Youngjoon Han2

  • 1Department of Electronic Engineering, Soongsil University, Seoul 06978, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces Keypoint Features for improved facial expression recognition (FER) by extracting detailed information from facial landmarks. The novel approach enhances emotion detection accuracy by focusing on critical facial regions.

Keywords:
deep neural networkface alignmentfacial expression recognitionfeature attention

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Facial expression recognition (FER) is crucial for human-computer interaction.
  • Face alignment preprocessing is vital for accurate FER by normalizing facial images.
  • Existing FER methods struggle to effectively utilize specific facial region information.

Purpose of the Study:

  • To propose a novel method for enhancing FER performance using Keypoint Features.
  • To improve the utilization of facial landmark information in FER.
  • To develop robust and effective feature extraction techniques for FER.

Main Methods:

  • Extraction of Keypoint Features from feature maps at facial landmark coordinates.
  • Implementation of Keypoint Feature regularization via landmark perturbation for robustness.
  • Application of an attention mechanism using representative Keypoint Features for performance enhancement.

Main Results:

  • Achieved high accuracy on AffectNet (68.17% - 7), RAF-DB (93.16%), and FERPlus (91.44%) datasets.
  • Demonstrated improved performance on RAF-DB (94.04%) and FERPlus (91.66%) with pretraining.
  • Validated the effectiveness of Keypoint Features in enhancing FER.

Conclusions:

  • The proposed Keypoint Features effectively leverage critical facial region information for FER.
  • The method offers a promising approach to advance FER technology.
  • Keypoint Features show potential comparable to existing state-of-the-art methods.