Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
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(a) (a1) (a2) (a3)<br />
(b) (b1) (b2) (b3)<br />
(c) (c1) (c2) (c3)<br />
Figure 3.6: (a), (b), (c): Typical examples <strong>of</strong> sample face images from the Yale, PIE<br />
and ORL face databases. The respective subband faces (a1-c1), (a2-c2) and (a3-c3)<br />
with the approximations at level-4, level-5 and level-6 suppressed.<br />
In the following section, we explain and describe the algorithm for selecting subject-<br />
specific optimal subbands and the criteria used for searching optimal values <strong>of</strong> l and<br />
k to generate the best subband face.<br />
3.3 Proposed Methods for Subject-Specific Subband Selection<br />
In this section, we will describe four different criteria and the algorithm used for<br />
subject-specific subband selection. As every person has a unique facial structure<br />
with respect to others, self-shadowing and other factors such as outline, appear-<br />
ance etc. provide a strong basis for the existence <strong>of</strong> a separate subband (Ali − Aki )<br />
for each person. Precisely, the output <strong>of</strong> subband selection algorithm will be C<br />
(li, ki) pairs, where i = 1, 2..., C, C being the number <strong>of</strong> subjects. We calculate<br />
two measures called goatishness and lambishness [34] for each subject and for each<br />
l and k pair where l and k varies from l = 0, 1, ..., L and k = (l + 1), ....., K. The<br />
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