Automated facial expression recognition based on FACS action units
Lien, J.J.
Kanade, T.
Cohn, J.F.
Ching-Chung Li
Dept. of Electr. Eng., Pittsburgh Univ., PA;
This paper appears in: Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on
Publication Date: 14-16 Apr 1998
On page(s): 390-395
Meeting Date: 04/14/1998 - 04/16/1998
Location: Nara, Japan
ISBN: 0-8186-8344-9
References Cited: 21
INSPEC Accession Number: 5920441
Digital Object Identifier: 10.1109/AFGR.1998.670980
Current Version Published: 2002-08-06
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
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