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Abstract:
The Facial Action Coding System (FACS) is an objective method
for quantifying facial movement in terms of component actions. This
system is widely used in behavioral investigations of emotion,
cognitive processes, and social interaction. The coding is
presently performed by highly trained human experts. This paper
explores and compares techniques for automatically recognizing
facial actions in sequences of images. These methods include
unsupervised learning techniques for finding basis images such as
principal component analysis, independent component analysis and
local feature analysis, and supervised learning techniques such as
Fisher's linear discriminants. These data-driven bases are compared
to Gabor wavelets, in which the basis images are predefined. Best
performances were obtained using the Gabor wavelet representation
and the independent component representation, both of which
achieved 96\% accuracy for classifying twelve facial actions. The
ICA representation is 90\% more computationally efficient than the
Gabor representation due to the large difference in the number of
kernels. The results provide evidence for the importance of using
local image bases, high spatial frequencies, and statistical
independence for classifying facial actions.
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