Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
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Table 5.10: Combined performance (in Percentage Accuracy) with ORL and F PB<br />
databases for different decision fusion strategies.<br />
T estSet Sum Max Min Product DT DS PLDA PNLDA<br />
T est11 93.00 92.67 88.00 92.33 83.00 82.33 96.00 97.67<br />
T est12 88.75 88.33 70.83 81.67 78.33 80.83 92.50 92.92<br />
T est13 87.22 85.56 65.56 78.33 77.22 82.22 91.11 92.78<br />
T est14 91.11 84.44 62.78 78.89 80.00 85.55 94.44 92.78<br />
T est21 92.67 86.00 81.67 91.33 85.00 84.67 98.33 98.67<br />
T est22 85.83 80.83 69.17 77.08 83.75 83.75 96.25 97.08<br />
T est23 85.56 82.22 64.44 74.44 79.44 79.44 96.67 96.67<br />
T est24 88.89 78.89 65.00 76.67 80.56 87.22 97.22 97.22<br />
class-specific information. We have an exception in the 3 rd row <strong>of</strong> Table 5.8, where<br />
max rule outperforms the rest. This validates the efficiency <strong>of</strong> our approach <strong>of</strong> decision<br />
combination over already existing fusion techniques. As evident from Tables 5.7-5.10,<br />
sum rule is the closest competitor <strong>of</strong> our method in most <strong>of</strong> the cases. Product rule<br />
is the next best. Decision Template (DT) and Dempster-Shafer (DS) combination<br />
fail to work well because <strong>of</strong> their blind belief on all classifiers to provide class-specific<br />
information in their outputs.<br />
5.5 Conclusion<br />
As unimodal biometry is going to saturate in near feature in terms <strong>of</strong> performance<br />
and is having several limitations like nonuniversality, spo<strong>of</strong> attacks and noisy data,<br />
multimodal biometry has become a hot field for research. Due to the heterogeneous<br />
nature <strong>of</strong> the features extracted from different modalities, it is very difficult to com-<br />
bine them at feature level. So decision level fusion has become an obvious choice<br />
for combining different modalities due to the homogeneity <strong>of</strong> multiple classifiers at<br />
decision level.<br />
Here we have tried to strengthen each classifier by using training data at fusion<br />
level, depending upon the presence <strong>of</strong> class-specific information in classifier output<br />
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