Recognition of facial expressions - Knowledge Based Systems ...

483: 138 004 5+25+27 3 3 3 6 4 2 2 6 6 2
484: 138 005 6+7+12+25 3 3 2 2 1 1 2 1 2 6
485: 138 006 6+7+12Y 3 3 2 4 1 1 2 1 2 6
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Listing 4. The parameter discretization result
The more classes for the discretization process are, the higher the final emotion
recognition rate was achieved. It was also found that after a certain value, the additional
recognition percent obtained as result of increasing the number of the classes decreases.
The discretization process on parameters was mainly implied by the presence of Bayesian
Belief Network reasoning. There was no need for discretization of parameters in the case
only neural network computations are run. In that case, for instance, it would have
determined a certain number of bits any value of the parameters could be represented and
the values could directly be encoded on the neurons in the input layer. Another option
would have been to work directly with values of the neurons in a given interval. Any
value taken by the parameters could be scaled to the correspondent in the interval and
encoded in the proper input neuron.
In the case of Bayesian Belief Networks, the number of classes determines the number of
states of each parameter.
In the testing stage of the system, a new set of Facial Characteristic Points is provided by
the FCP detection module. Based on the values, a set of parameter values is obtained
following the computations. In case there is any value exceeding the limits of the interval,
for one of the parameters, the number according to the nearest class is used instead.
The BBN based reasoning is done by setting the class values of the parameters as
evidence in the network and by computing the anterior probabilities of the parameters.
Finally, the probabilities according to each emotional class are read from the proper
parameter.
Facial Expression Assignment Application
Once the FCPs were specified in the set of 485 images and the 10 parameter values were
computed, a software application processed each sequence of FACS for assigning the
correct emotion to each face.
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