Refactoring Facial Expressions: An Automatic Analysis of Natural Occurring Facial Expressions in Iterative Social Dilemma

Abstract

Many automatic facial expression recognizers now output individual facial action units (AUs), but several lines of evidence suggest that it is the combination of AUs that is psychologically meaningful: e.g., (a) constraints arising from facial morphology, (b) prior published evidence, (c) claims arising from basic emotion theory. We performed factor analysis on a large data set and recovered factors that have been discussed in the literature as psychologically meaningful. Further we show that some of these factors have external validity in that they predict participant behaviors in an iterated prisoner's dilemma task and in fact with more precision than the individual AUs. These results both reinforce the validity of automatic recognition (as these factors would be expected from accurate AU detection) and suggest the benefits of using such factors for understanding these facial expressions as social signals.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2018
Accession Number
AD1159512

Entities

People

  • Giota Stratou
  • Job Van Der Schalk
  • Jonathan Gratch
  • Rens Hoegen

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Automatic
  • Computer Programming
  • Computers
  • Cooperation
  • Data Mining
  • Data Sets
  • Factor Analysis
  • Human Behavior
  • Human-Machine Interaction
  • Literature
  • Machine Learning
  • Motivation
  • Negotiations
  • Personality
  • Psychology
  • Recognition
  • Social Psychology

Fields of Study

  • Psychology

Readers

  • Astronomy/Astrophysics
  • Systems Analysis and Design
  • Trauma or Military Medicine