The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

Automated recognition of facial expression is an important addition to computer vision research because of its relevance to the study of psychological phenomena and the development of human-computer interaction (HCI). We developed a computer vision system that automatically recognizes individual action units or action unit combinations in the upper face using hidden Markov models (HMMs). Our approach to facial expression recognition is based an the Facial Action Coding System (FACS), which separates expressions into upper and lower face action. We use three approaches to extract facial expression information: (1) facial feature point tracking; (2) dense flow tracking with principal component analysis (PCA); and (3) high gradient component detection (i.e. furrow detection). The recognition results of the upper face expressions using feature point tracking, dense flow tracking, and high gradient component detection are 85%, 93% and 85%,

respectively.

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): Face Group and Component Analysis

Associated Project(s): Facial Expression Analysis and Face Databases

Text Reference

Jenn-Jier James Lien, Takeo Kanade, Jeffrey Cohn, and Ching-Chung Li, "Automated Facial Expression Recognition Based on FACS Action Units," Third IEEE International Conference on Automatic Face and Gesture Recognition, April, 1998, pp. 390-395.

BibTeX Reference

@inproceedings{Lien_1998_1018,
   author = "Jenn-Jier James Lien and Takeo Kanade and Jeffrey Cohn and Ching-Chung Li",
   title = "Automated Facial Expression Recognition Based on FACS Action Units",
   booktitle = "Third IEEE International Conference on Automatic Face and Gesture Recognition",
   pages = "390-395",
   month = "April",
   year = "1998",
}

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