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A Review on Automatic Facial Expression Recognition Systems Assisted by Multimodal Sensor Data.

Najmeh Samadiani1, Guangyan Huang2, Borui Cai3

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Summary

Facial Expression Recognition (FER) accuracy drops significantly in real-world settings due to challenges like lighting and head pose. This survey explores multimodal sensors to enhance FER systems for more reliable emotion detection outside the lab.

Keywords:
emotional expression recognitionfacial expression recognition (FER)multimodal sensor datareal-world conditionsspontaneous expression

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Area of Science:

  • Computer Science, Artificial Intelligence, Affective Computing, Human-Computer Interaction

Background:

  • Facial Expression Recognition (FER) systems achieve high accuracy in controlled lab environments (approx. 97%).
  • Real-world FER applications face significant accuracy barriers (approx. 50%) due to unconstrained environments.
  • Key challenges include illumination variation, head pose changes, and subject dependence, which limit traditional image/video analysis.

Purpose of the Study:

  • To survey and categorize sensors that can augment traditional image/video-based FER systems.
  • To address the accuracy limitations of FER in real-world, unconstrained environments ('in the wild').
  • To propose a framework for multimodal sensor fusion to improve emotion recognition reliability.

Main Methods:

  • Categorization of sensors into three groups: detailed-face sensors (e.g., eye-trackers), non-visual sensors (e.g., audio, depth, EEG), and target-focused sensors (e.g., infrared thermal).
  • Review of multimodal sensor fusion techniques for emotion recognition systems.
  • Comparative analysis of prominent multimodal FER approaches, including datasets, advantages, and limitations.

Main Results:

  • Multimodal sensor integration offers a promising solution to overcome the limitations of pure visual FER.
  • Detailed-face, non-visual, and target-focused sensors provide complementary information to improve robustness against real-world variations.
  • A proposed framework using multimodal sensor data can provide more complete emotional information for enhanced FER.

Conclusions:

  • Multimodal sensor fusion is crucial for developing accurate and reliable FER systems for real-world applications.
  • The surveyed sensor categories and fusion methods offer pathways to significantly improve FER performance in unconstrained settings.
  • Future research should focus on efficient multimodal system design for practical, 'in the wild' emotion recognition.