Master Thesis - Department of Computer Science

We have associated each face database with a fingerprint database and thus ob-
tained 4 possible combinations: (i) PIE & F PA, (ii) PIE & F PB, (iii) ORL & F PA
and (iv) ORL & F PB. For each combination of a face and fingerprint database, total
number of virtual test samples for a subject are obtained by all possible combina-
tion of test samples provided for the corresponding subject from face and fingerprint
databases. For example, for the combination of PIE and F PA databases the number
of virtual test samples per person is 170 (=34*5). We have created two and four dif-
ferent test sets (also corresponding train and validation) for each face and fingerprint
databases respectively. T esti denotes i th test set for any database. For a particular
face and fingerprint database combination, T estij represents the combination of i th
test set from face and j th test set from fingerprint database. PCA [123] is used for
extracting features from face. We have used PCA to keep the performance of base
classifier for face reasonably weak and thus allow some space for fusion techniques to
perform better than that of the base classifiers. The algorithm for fingerprint match-
ing have been adopted from existing literature [55] [100] [49], using elastic graph
matching technique over a minutiae feature set.
As described in Section 5.2 and in Section 5.3, the distribution of a database
should be in four disjoint sets as train, validation1, validation2 and test sets. Three
set pairs {Train, validation1}, {Train, validation2} and {Train, Test} in feature
space, create train, validation and test response vector sets in classifier output space.
Validation response vector set is needed to check the availability of class specific in-
formation and applicability of LDA or nonparametric LDA in classifier output space.
If the application of LDA or nonparametric LDA-based eigenmodel produces better
performance than that of direct classifier output on validation response vector set,
then we can utilize the scope of strengthening our base classifiers using LDA and
nonparametric LDA. Intuitively, a stronger base classifier always helps to enhance
combination performance, as it helps to enhance mean accuracy of a classifier en-
semble it belongs to. This is evident from our experimental results provided in this
section. In our experimentation, we assume the test set to be same as validation2 for
the following reasons:
• The number of samples per class for fingerprint is too small to divide in four
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