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6 CONCLUSIONS
In the paper, we have shown that the coherent move-
ment extracted from dense Optical Flow method by
considering the facial movement hypothesis achieves
state-of-the-art performance on both facial full-
expression and micro-expression databases. The
magnitude and direction constraints are estimated in
order to reduce the noise induced by lighting changes
and small head motions over time. The proposed ap-
proach adapts well on both full-expressions (CK+)
and micro-expressions (CASME2). The only adjust-
ment concerning the magnitude intervals is actually
related to the nature of expression. The other param-
eters common to both experiences have been selected
empirically and deserve specific attention in future
experiments.
Our current approach is used only in near-frontal-
view recordings where the presence of occlusions,
fast head motion and lightning variation is practi-
cally zero. The next step consist in adapting our
method to the domain of spontaneous facial expres-
sion recognition. To address this situation, a normal-
ization method will be necessarily used. However, it
must be kept in mind that any change made in the
facial picture has important side-effects on the Op-
tical Flow. Despite the wealth of research already
conducted, no method is capable of dealing with all
issues at a time. We believe that the normalization
approaches based on facial components or shape are
not adapted to Optical Flow as facial deformation will
impact Optical Flow computation by inducing motion
distortion. So rather than considering the normaliza-
tion in the field of facial components, efforts should
instead be focused on the Optical Flow domain.
ACKNOWLEDGEMENTS
This research has been partially supported by the FUI
project MAGNUM 2.
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