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