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