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Recognizing Action Units for Facial Expression Analysis.

Ying-Li Tian1, Takeo Kanade2, Jeffrey F Cohn3

  • 1Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213. yltian@cs.cmu.edu.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|September 12, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces an Automatic Face Analysis (AFA) system that recognizes fine-grained facial expressions using the Facial Action Coding System (FACS). The AFA system achieves high accuracy in identifying subtle facial changes, offering a more nuanced approach to emotion recognition.

Keywords:
AU combinationsComputer visionaction unitsfacial action coding systemfacial expression analysismultistate face and facial component modelsneural network

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biometrics

Background:

  • Traditional facial expression systems focus on a limited set of prototypic emotions, which are rarely observed.
  • Human emotions are often conveyed through subtle, discrete changes in facial features rather than distinct expressions.

Purpose of the Study:

  • To develop an Automatic Face Analysis (AFA) system for detailed facial expression recognition.
  • To move beyond prototypic expressions and analyze fine-grained changes using action units (AUs) from the Facial Action Coding System (FACS).

Main Methods:

  • The AFA system analyzes both permanent (brows, eyes, mouth) and transient (facial furrows) features from face image sequences.
  • Multistate models are used for tracking and modeling facial components like lips, eyes, brows, cheeks, and furrows.
  • Parametric descriptions of facial features are extracted and used to recognize upper and lower face AUs, individually or in combination.

Main Results:

  • The AFA system achieved average recognition rates of 96.4% for upper face AUs and 96.7% for lower face AUs.
  • Excluding neutral expressions, recognition rates were 95.4% for upper face AUs and 95.6% for lower face AUs.
  • The system demonstrated generalizability across independent image databases coded by different research teams.

Conclusions:

  • The developed AFA system effectively recognizes fine-grained facial expressions based on FACS action units.
  • The system offers a more comprehensive approach to facial expression analysis compared to systems relying on prototypic expressions.
  • The demonstrated accuracy and generalizability support the system's utility in various applications requiring detailed facial expression understanding.