Master Thesis - Department of Computer Science

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