Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
Create successful ePaper yourself
Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.
<strong>of</strong> the five subspace methods on them. During the identification phase, we create all<br />
subject-specific subband faces T(li, ki), i = 1, 2, ...., C for the test image, T . Then the<br />
C different subband faces are projected onto the corresponding subspaces to obtain<br />
the distances from C different classes. More specifically, i th test subband face T(li, ki) is<br />
projected on i th subspace LSAi to obtain the (Euclidean) distance <strong>of</strong> the test sample<br />
from i th class. A process <strong>of</strong> normalization (using min-max, with score value in range<br />
[0, 1]) is performed separately on each subspace to compare the C different distances<br />
computed in C different subspaces for obtaining the class label based on minimum<br />
or maximum membership rule. Let D = [d1, . . . , di, . . . , dC] be the vector <strong>of</strong> distance<br />
values obtained from C different subspaces after normalization. Now the class label<br />
for test sample T , L(T ), is obtained by using minimum membership rule on D.<br />
L(T ) = arg min<br />
c dc. (3.11)<br />
The method for verification is simpler than the identification stage as we claim a<br />
person’s identity and allow the system to verify it. Only one subband face T(lc, kc) is<br />
generated for the test sample T , where the claimed identity is class c. Then T(lc, kc) is<br />
projected on the c th subspace LSAc to obtain the distance measure from c th class. The<br />
calculated distance measure is checked against a predefined threshold value to produce<br />
the output as acceptance or rejection. In the next section, we provide experimental<br />
results for our proposed method and also compare our method with the approach <strong>of</strong><br />
Ekenel’s multiresolution face recognition [36] (the closest and recent competitor <strong>of</strong><br />
our approach).<br />
3.4 Experimental Results<br />
3.4.1 Databases Used<br />
In this chapter, three face databases were used for experiments - Yale, PIE and ORL,<br />
where:<br />
• The Yale face database has 15 subjects with 11 samples each, having variations<br />
in expression and illumination for each subject.<br />
• A subset <strong>of</strong> the PIE (Pose Illumination and Expression) face database [112, 5]<br />
with 60 subjects, where only the frontal poses (camera: c27) were used. For<br />
60