Recognition of facial expressions - Knowledge Based Systems ...

Principal Component Analysis
The need for the PCA technique came from the fact that it was necessary to have a
classification mechanism for handling special areas on the surface of the face. There were
three areas of a high importance for the analysis. The first is the area between the
eyebrows. For instance, the presence of wrinkles in that area can be associated to the
tension in facial muscles ‘Corrugator supercilii’/’Depressor supercilii’ and so presence of
Action Unit 4, for the ‘lowered brow’ state. The second area is the nasolabial area. There
are certain facial muscles whose changes can produce the activation of certain Action
Units in the nasolabial area. The Action Unit 6 can be triggered by tension in facial
muscle ‘Orbicularis oculi, pars orbitalis’. In the same way, tension in facial muscle
‘Levator labii superioris alaquae nasi’ can activate Action Unit 9 and strength of facial
muscle ‘Levator labii superioris’ can lead to the activation of Action Unit 10.
The last visual area analyzed through the image processing routines is that of the chin.
The tension in the facial muscle ‘Mentalis’ is associated to the presence of Action Unit
17, ‘raised chin’ state.
The PCA technique was used to process the relatively large images for the described
facial areas. The size of each area was expressed in terms of relative value comparing to
the distance between the pupils. That was used for making the process robust to the
distance the person stands from the camera, and person-independent.
The analyze was done separately, for each facial area. Every time there was available a
set of 485 n-size vectors where n equals the width of the facial area multiplied by the
height. In the common case, the size of one sample vector is of the order of few thousand
values, one value per pixel. The facial image space was highly redundant and there were
large amounts of data to be processed for making the classification of the desired
emotions. Principal Components Analysis (PCA) is a statistical procedure which rotates
the data such that maximum variability is projected onto the axes. Essentially, a set of
correlated variables are transformed into a set of uncorrelated variables which are ordered
by reducing variability. The uncorrelated variables are linear combinations of the original
variables, and the last of these variables can be removed with minimum loss of real data.
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