Recognition of facial expressions - Knowledge Based Systems ...

performing classification of facial expressions, based on the images received from the
first application. The reasoning application was designed as a server-side application. The
connectivity among the present components is as it is presented in the figure.
Figure 20. The design of the system
The two applications send data to each-other through a TCP/IP connection. The media
client acts like a bridge between the capturing device and the emotion classification
application. It only sends captured images to the other side of the network. The server
application can send back parameters concerning the rate of capturing, the frame size or
parameters related to the image, as the contrast, brightness, etc.
The server application receives the images, one after another and put them on a
processing queue. A thread is assumed to take the first image in the queue and to pass it
to a sequence of processing modules. The first module is that of eye location recovery.
Based on the detection result, the positions of all the other facial characteristic points are
extracted. The next module is that of computing the values of some parameters according
to the used model for recognition. The procedure implies analyzes of distances and angles
among given facial characteristic points. The actual recognition module consists in a
Bayesian Belief Network that already contains the proper values for all the associated
probability tables for the nodes. The reasoning is done by setting as evidence the states
for all the model parameters according to the discretization algorithm and by computing
the anterior probabilities for the parent parameters. The parameter that encodes the
Expression node contains six distinct states, one for each basic emotion class. By
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