Recognition of facial expressions - Knowledge Based Systems ...

not taking into account also the pixels with high probability to be part of the same are on
the eye surface. The algorithm computes the values of mean and variance for the pixels
presented as in Figure 24.
Figure 24. The eye-area searched
The values defining the area to be analyzed are parameters of the module. When the
previous procedure is finished for all the candidate pixels, a new threshold procedure for
the mean and variance is applied. The threshold for the variance has the goal to remove
all the pixels whose surrounding area is not compact with respect to the intensity. The
threshold for the mean is assumed to remove the candidate pixels whose intensity of all
surrounding pixels is high. The procedure is not applied on the image resulting from the
previous processing steps, but on the original image.
After the selection procedure only a few pixels remained that comply with the encoded
specifications. One way of finding the position of the eyes is to take the pixels that have
the highest intensity far enough from each-other from the remaining candidates queue.
The last two steps can be replaced with a simple back-propagation neural network for
learning the function that selects only the proper pixels for eye location based on the
value of mean and variance of the surrounding area (Figure 25).
Figure 25. The eyes area found
The major limitation of the algorithm is that it is not robust enough. There are several
cases when the results are not as expected. For instance when the eyes are closed or
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