Classifying facial actions
Donato, G.
Bartlett, M.S.
Hager, J.C.
Ekman, P.
Sejnowski, T.J.
Digital Persona, Redwood City, CA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Oct 1999
Volume: 21,
Issue: 10
On page(s): 974-989
ISSN: 0162-8828
References Cited: 66
CODEN: ITPIDJ
INSPEC Accession Number: 6406666
Digital Object Identifier: 10.1109/34.799905
Current Version Published: 2002-08-06
Abstract
The facial action coding system (FAGS) is an objective method for
quantifying facial movement in terms of component actions. This paper
explores and compares techniques for automatically recognizing facial
actions in sequences of images. These techniques include: analysis of
facial motion through estimation of optical flow; holistic spatial
analysis, such as principal component analysis, independent component
analysis, local feature analysis, and linear discriminant analysis; and
methods based on the outputs of local filters, such as Gabor wavelet
representations and local principal components. Performance of these
systems is compared to naive and expert human subjects. Best
performances were obtained using the Gabor wavelet representation and
the independent component representation, both of which achieved 96
percent accuracy for classifying 12 facial actions of the upper and
lower face. The results provide converging evidence for the importance
of using local filters, high spatial frequencies, and statistical
independence for classifying facial actions
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