Master Thesis - Department of Computer Science

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