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Emotional expression encompasses how individuals convey their emotions through verbal communication and non-verbal cues. These non-verbal actions include facial expressions, body language, and physical gestures, such as frowning or smiling. Among these, facial expressions play a crucial role in emotional expression and are understood universally, indicating a biological basis for how humans communicate emotions.
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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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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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Multi-view emotional expressions dataset using 2D pose estimation.

Mingming Zhang1,2, Yanan Zhou1,2, Xinye Xu1,2

  • 1Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, 116029, Liaoning, China.

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|September 22, 2023
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Summary
This summary is machine-generated.

This study introduces the Multi-view Emotional Expressions Dataset (MEED), a large collection of 2D videos capturing natural human body language and emotions. MEED provides crucial data for advancing research in affective computing and human-computer interaction.

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Area of Science:

  • Computer Science
  • Psychology
  • Neuroscience

Background:

  • Human body expressions are vital for conveying emotions and intentions, often surpassing verbal communication.
  • Existing datasets for body expressions primarily use motion capture, lacking naturalistic 2D video data.
  • There is a need for accessible, large-scale datasets of natural body language for research.

Purpose of the Study:

  • To introduce the Multi-view Emotional Expressions Dataset (MEED), a novel resource for studying body language.
  • To provide a comprehensive dataset of naturalized emotional body expressions captured via 2D video.
  • To facilitate research in affective computing, human-computer interaction, and related fields.

Main Methods:

  • Collected 4102 videos of 22 actors displaying six emotions (anger, disgust, fear, happiness, sadness, surprise) and neutral expressions.
  • Recorded movements from three viewpoints: left, front, and right.
  • Utilized 2D pose estimation to generate corresponding pose data (397,809 PNG and JSON files).

Main Results:

  • The Multi-view Emotional Expressions Dataset (MEED) comprises over 150 GB of data.
  • Includes synchronized video and 2D pose estimation data for diverse emotional expressions.
  • Offers a rich resource for computational analysis of human emotional body language.

Conclusions:

  • MEED addresses the gap in readily available, naturalistic 2D body expression datasets.
  • The dataset is expected to significantly benefit research in affective computing, HCI, social neuroscience, and psychiatry.
  • Facilitates the development of more sophisticated emotion recognition systems and human-centric AI.