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Macro- and Micro-Expressions Facial Datasets: A Survey.

Hajer Guerdelli1,2, Claudio Ferrari3, Walid Barhoumi1,4

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PubMed
Summary
This summary is machine-generated.

This survey reviews over eighty facial expression datasets, focusing on spontaneous, in-the-wild data for automatic facial expression recognition (FER) and neural network training. It aids researchers in selecting optimal datasets for FER applications.

Keywords:
applications of facial expression datasetsfacial expression recognitionmacro-expressions datasetsmicro-expressions datasets

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Automatic facial expression recognition (FER) is crucial for numerous applications.
  • Effective FER solutions, especially for neural network training, require a comprehensive understanding of available datasets.
  • Existing research increasingly focuses on spontaneous, real-world expressions ('in-the-wild' datasets).

Purpose of the Study:

  • To provide a comprehensive review of over eighty facial expression datasets.
  • To analyze both macro- and micro-expressions, with a focus on spontaneous and in-the-wild data.
  • To assist researchers in selecting appropriate datasets by highlighting their pros and cons.

Main Methods:

  • Systematic review of existing facial expression datasets.
  • Categorization of datasets based on expression type (macro/micro) and context (spontaneous/in-the-wild).
  • Analysis of dataset characteristics, including strengths and weaknesses.

Main Results:

  • Identification and review of more than eighty facial expression datasets.
  • Emphasis on spontaneous and in-the-wild datasets, reflecting current research trends.
  • Discussion of potential applications and limitations of each dataset.

Conclusions:

  • A clear overview of facial expression datasets is vital for developing and evaluating FER systems.
  • The survey facilitates informed dataset selection for researchers in the field.
  • Understanding dataset characteristics is key to advancing FER technology.