The locations of the mouth and the two eye regions give a fairly stable measure of the size of a face. We can use these feature points to compute EM, the distance from the mid-point between the eyes to the mouth. From sample measurements, the value of EM was found to be reliable enough to predict the radius of the iris of a subject's face. The iris radius is typically expected to fall between 5% and 15% of the value of EM.
Consequently, the real-time symmetry transform is reset to use annular
sampling regions that cover a radius between 5% and 15% of the value of
EM. The transform is then utilized to compute two small interest maps around
the previously located eye regions. The peaks of these interest maps indicate
possible positions of the left and right iris. Figure shows
the resulting iris loci.
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The search space for the iris is centered around the old eye locus and is a
square with length 25%
on each side. Figure
shows
the actual search space windows with the best iris position represented with
a + symbol. The strongest interest peak in that window is used as the
iris position. The interest map is thresholded to discard all peaks which
trigger symmetry values below 25% of the maximum possible output. This extra
threshold allows us to avoid triggering the iris finder with other structures
such as the eye brows. These and other objects generate a weak response and
the iris finder might erroneously converge to their loci if the iris is not
clearly visible (i.e. subject is squinting). Thus, the threshold allows us to
report the absence of an iris in the search space if the peak response is too
weak. Consequently, no valid peaks in the interest maps are found, and the
iris localization function can merely default to the previously calculated
position of the eye region. Therefore, if the individual in the image is
squinting or the eyes are not clearly visible, we use the large, coarse
eye-blob detection output instead of the iris finder as the position of the
iris.