Recognition of facial expressions - Knowledge Based Systems ...

there has been used different numbers of the hidden neurons. The output layer manages
the encoding of the six basic facial expression classes. It basically contains three output
neurons. There has also been conducted some experiments for recognition of the 22
Action Units (AUs) and for each Action Unit there exists a distinct neuron for encoding
the state of presence or absence. The general architecture to the ANNs used for
experiments is as presented:
Knowledge Building in an ANN
When the system learns a new association in the input/output space, a measure is used to
give the degree of the improvement or of the distance the ANN is from the point it
recognize all the samples without any mistake.
The learning algorithm is based on a gradient descent in error space, the error being
defined as:
The term
E =
P
E p
E
p
is the error for one input pattern, and
1
E
p
= ( ti
− a
2
The weights are adjusted according to the gradient of error
i
∆w = −η
∇E
The term η is a constant scaling factor defining the step-size.
The weight change for the connection from unit i to unit j, of this error gradient can be
i
)
2
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