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Understanding Naturalistic Facial Expressions with Deep Learning and Multimodal Large Language Models.

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  • 1Department of Experimental Psychology, University College London, London WC1H 0AP, UK.

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Summary
This summary is machine-generated.

This paper reviews advanced facial expression recognition (FER) toolboxes and multimodal large language models (MLLMs) for affective computing. These systems enhance emotion recognition in real-world settings, paving the way for context-aware emotion models.

Keywords:
automatic facial expression recognitiondeep learningmultimodal large language modelnaturalistic context

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Area of Science:

  • Affective computing
  • Artificial intelligence
  • Psychology

Background:

  • Facial expression recognition (FER) research in naturalistic contexts faces challenges like illumination and head pose variations.
  • Existing FER toolboxes often struggle with real-world complexities, impacting recognition accuracy.

Purpose of the Study:

  • To provide a comprehensive overview of current affective computing systems for FER in naturalistic settings.
  • To explore the potential of multimodal large language models (MLLMs) in advancing affective science and emotion recognition.

Main Methods:

  • Review of user-friendly FER toolboxes featuring state-of-the-art deep learning models, including their architectures, datasets, and performance.
  • Discussion of multimodal large language models (MLLMs) and their capabilities in FER and contextual variable quantification.

Main Results:

  • Advanced FER toolboxes demonstrate robustness against real-world variations, improving recognition accuracy.
  • MLLMs show human-level FER capabilities and enable context-aware emotion inferences by quantifying contextual variables.

Conclusions:

  • Current FER toolboxes offer improved performance in naturalistic contexts.
  • MLLMs are poised to revolutionize affective science by facilitating the development of contextualized emotion models.