NDLI: A framework for automated measurement of the intensity of non-posed Facial Action Units

Content Provider	IEEE Xplore Digital Library
Author	Mahoor, M.H. Cadavid, S. Messinger, D.S. Cohn, J.F.
Copyright Year	2009
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Denver, Denver, CO, USA (Mahoor, M.H.) \|\| Dept. of Electr. & Comput. Eng., Univ. of Miami, Coral Gables, FL, USA (Cadavid, S.) \|\| Dept. of Psychol., Univ. of Miami, Coral Gables, FL, USA (Messinger, D.S.) \|\| Dept. of Psychol., Univ. of Pittsburgh, Pittsburgh, PA, USA (Cohn, J.F.)
Abstract	This paper presents a framework to automatically measure the intensity of naturally occurring facial actions. Naturalistic expressions are non-posed spontaneous actions. The facial action coding system (FACS) is the gold standard technique for describing facial expressions, which are parsed as comprehensive, nonoverlapping action units (Aus). AUs have intensities ranging from absent to maximal on a six-point metric (i.e., 0 to 5). Despite the efforts in recognizing the presence of non-posed action units, measuring their intensity has not been studied comprehensively. In this paper, we develop a framework to measure the intensity of AU12 (lip corner puller) and AU6 (cheek raising) in videos captured from infant-mother live face-to-face communications. The AU12 and AU6 are the most challenging case of infant's expressions (e.g., low facial texture in infant's face). One of the problems in facial image analysis is the large dimensionality of the visual data. Our approach for solving this problem is to utilize the spectral regression technique to project high dimensionality facial images into a low dimensionality space. Represented facial images in the low dimensional space are utilized to train support vector machine classifiers to predict the intensity of action units. Analysis of 18 minutes of captured video of non-posed facial expressions of several infants and mothers shows significant agreement between a human FACS coder and our approach, which makes it an efficient approach for automated measurement of the intensity of non-posed facial action units.
Starting Page	74
Ending Page	80
File Size	243129
Page Count	7
File Format	PDF
ISBN	9781424439942
ISSN	21607508
DOI	10.1109/CVPRW.2009.5204259
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Image analysis Shape Face recognition Humans Psychology Support vector machine classification Behavioral science Face detection Image texture analysis
Content Type	Text
Resource Type	Article

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