Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
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argument is invalid in the context <strong>of</strong> the issue addressed in this chapter. We need<br />
completely different techniques to improve individual classifiers in feature space. For<br />
example, we cannot use the same new technique to improve both face and fingerprint<br />
classification. But here we are applying the same framework as a preprocessing step<br />
to all classifiers output responses, which are homogeneous.<br />
Table 5.6: Base classifier’s performance (in Percentage Accuracy) using nonparamet-<br />
ric LDA for the face and fingerprint databases (two each).<br />
Test Set PIE ORL F PA F PB<br />
T est1 85.00 86.67 66.00 30.00<br />
T est2 83.52 96.67 67.50 62.50<br />
T est3 - - 60.00 56.67<br />
T est4 - - 70.00 56.67<br />
The combined results for each combination <strong>of</strong> face and fingerprint databases are<br />
tabulated in separate tables. Table 5.7-5.10 show the combined performance for PIE<br />
& F PA, PIE & F PB, ORL & F PA and ORL & F PB database combinations. PLDA<br />
and PNLDA are our pair <strong>of</strong> proposed methods using LDA and nonparametric LDA<br />
respectively. Results on all database combinations have shown the superiority <strong>of</strong> our<br />
method over existing fusion techniques. The superiority <strong>of</strong> our method over it’s best<br />
competitor is explained by choosing four test cases (see in Table 5.3), which exhibit<br />
situations (performance) where: (1) both face and fingerprint classifiers are good,<br />
(2) both are bad, (3) face classifier is good while fingerprint classifier is bad and (4)<br />
the reverse <strong>of</strong> case (3). Four test cases have been used, based on the performance <strong>of</strong><br />
individual base classifiers, which are explained below:<br />
1. Both classifiers are good: The base classifiers performance for T est1 <strong>of</strong><br />
ORL and T est1 <strong>of</strong> F PA are 83.33% and 86% respectively. Both PLDA and<br />
PNLDA provide maximum accuracy <strong>of</strong> 98.00% as compared to 95.67% as given<br />
by product rule (see 1 st row <strong>of</strong> Table 5.9).<br />
2. Both classifiers are bad: The performance <strong>of</strong> base classifiers for T est2 <strong>of</strong><br />
ORL and T est3 <strong>of</strong> F PB are 76.67% and 60.00% respectively. Nonparametric<br />
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