Master Thesis - Department of Computer Science

sets.
• We put as much as possible samples per class in test set to make it far bigger
than other sets and also to maintain reasonably low accuracy on test set for
base classifiers.
• It can be easily argued that the cross validation test on test set will give us the
same decision as the case when cross validation check is done on validation2
set. This is because validation2 set can be treated as a subset of test set.
The relatively weak performance of base classifiers (as shown in Table 5.4) allow
the fusion to demonstrate its utility. The state of the art classifiers for face and
fingerprint recognition are not used in this work as their performance leaves a nar-
row scope for fusion to improve upon them. We have performed experiments with
different cases, where: (i) Both base classifiers are comparable (both either bad or
good) in performance and (ii) One of the classifiers is less/more efficient in compar-
ison to the other. A few examples of such combinations are tabulated in Table 5.3.
Table 5.4 shows the performance as per Percentage Accuracy of face and fingerprint
base classifiers on all test sets for all the databases, as specified before.
Table 5.3: Some test cases to simulate different type (performance-wise) of classifier
combination.
Face Fingerprint Description
Set Performance Set Performance
T est1 of ORL 83.33 T est1 of F PA 86.00 Both good
T est2 of ORL 76.67 T est3 of F PB 60.00 Both bad
T est1 of PIE 81.47 T est4 of F PB 63.33
Face good and
Fingerprint bad
T est2 of ORL 76.67 T est1 of F PA 86.00 Face bad and
Fingerprint good
Table 5.5 and Table 5.6 show the performance of face and fingerprint classifiers
after applying LDA and nonparametric LDA on classifier output space for the same
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