Master Thesis - Department of Computer Science

LDA and LDA produce the same accuracy of 96.67%, whereas sum rule, the
next best method in this case, gives 85.56% (see second last row of Table 5.10).
3. Face classifier is good while fingerprint classifier is bad: The perfor-
mance of face and fingerprint classifier on T est1 of PIE and T est4 of F PB are
81.47% and 63.33% respectively. The combined performaces for this pair, us-
ing PLDA, PNLDA and sum rule are 88.92%, 88.92% and 87.65% (see 4 th row of
Table 5.8).
4. Face classifier is bad while fingerprint classifier is good: The perfor-
mance of face and fingerprint classifiers on T est2 of ORL and T est1 of F PA are
76.67% and 86.00% respectively. We obtained a maximum of 98.00% accuracy
for both PLDA and PNLDA, as compared to 95.00% accuracy provided by sum
rule (see 5 th row of Table 5.9).
Table 5.9: Combined performance (in Percentage Accuracy) with ORL and F PA
databases for different decision fusion strategies.
T estSet Sum Max Min Product DT DS PLDA PNLDA
T est11 95.33 84.67 90.00 95.67 88.00 87.00 98.00 98.00
T est12 92.50 85.00 82.52 89.58 87.08 88.75 96.67 97.08
T est13 91.67 85.00 80.00 87.22 89.44 90.56 97.22 96.67
T est14 91.11 84.44 76.67 86.67 92.77 92.77 97.22 96.67
T est21 95.00 76.67 87.00 93.00 85.67 88.33 98.00 98.00
T est22 90.42 76.67 82.50 88.33 84.17 85.83 96.25 95.83
T est23 89.44 76.67 80.00 86.67 87.22 88.33 95.56 95.00
T est24 87.78 76.67 76.67 84.44 90.56 90.00 95.00 95.56
In each of the cases described above, our proposed algorithm performs better than
other methods. We have gained a percentage accuracy of 11.11% using both LDA
and nonparametric LDA over their best competitor sum rule in test case 2, where
we have combined two bad performing classifiers. In addition, one can observe (in
general) that the performance of our proposed methods in the last two columns in
Tables 5.7-5.10 are always better than any of the other approaches which do not use
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