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Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
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Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
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FACS-Based Graph Features for Real-Time Micro-Expression Recognition.

Adamu Muhammad Buhari1, Chee-Pun Ooi1, Vishnu Monn Baskaran2

  • 1Faculty of Engineering, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Selangor, Malaysia.

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|August 30, 2021
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Summary
This summary is machine-generated.

This study introduces novel facial graph features for micro-expression (ME) recognition, achieving high accuracy with single-frame processing. The new method significantly reduces computational complexity for real-time applications.

Keywords:
emotion recognitionfacial expressionfeature extractionmicro-expressionreal-time classification

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biometrics

Background:

  • Micro-expression recognition accuracy has improved, but computational cost for real-time use remains a challenge.
  • Existing methods often require multiple frames, increasing processing time and complexity.
  • Facial Action Coding System (FACS) provides a framework for analyzing facial movements.

Purpose of the Study:

  • To develop a computationally efficient micro-expression recognition technique.
  • To introduce novel facial graph features for improved recognition accuracy.
  • To enable real-time micro-expression analysis using single-frame input.

Main Methods:

  • Proposed a new feature extraction technique using FACS-based graph features derived from 68 facial landmark points.
  • Computed graph features based on distances and gradients within Action Unit (AU) segments.
  • Implemented single-frame processing for micro-expression recognition.

Main Results:

  • Achieved up to 87.33% recognition accuracy with an F1-score of 0.87 on the SAMM dataset.
  • Demonstrated feature computation speed of 2 ms per sample on a Xeon E5-2650 processor.
  • Validated performance using leave-one-subject-out cross-validation.

Conclusions:

  • The proposed FACS-based graph features offer a computationally efficient and accurate solution for micro-expression recognition.
  • Single-frame processing significantly reduces the complexity and cost of real-time micro-expression analysis.
  • This approach advances the field by balancing accuracy and speed for practical applications.