Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
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Table 3.7: Performance <strong>of</strong> Ekenel’s multiresolution face recognition [36] for Yale, PIE<br />
and ORL databases based on the successful subbands determined on testing set.<br />
Yale PIE ORL<br />
Methods PRA EER PRA EER PRA EER<br />
Original Face 81.67 7.78 58.85 17.05 80.83 3.33<br />
Best Performing Subband 88.33 5.95 90.64 7.50 70.83 8.49<br />
Data Fusion 76.67 16.67 34.46 28.21 69.17 7.07<br />
Feature Fusion 73.33 20.00 23.91 32.42 71.67 7.68<br />
Decision Fusion-Sum Rule 85.00 8.38 88.65 3.24 70.00 7.38<br />
Decision Fusion-Product Rule 85.00 8.67 90.77 8.82 72.50 6.82<br />
Decision Fusion-Max Rule 81.67 10.00 66.09 49.67 67.50 7.5<br />
feature (subband) selection criterion is however different. Then those selected sub-<br />
bands are combined at data, feature and decision level, as described in multiresolution<br />
approach [36] to obtain the results in Table 3.9.<br />
For Yale database, original image with PCA and L2 norm gives 81.67%. Experi-<br />
mentation on subband selection for fusion shows that A1, A2 and A3 (see Table 3.8)<br />
perform same as A0 (original image) on validation set. So A1, A2 and A3 are com-<br />
bined at data, feature and decision level and the performance was observed on the<br />
testing set. Note that, any <strong>of</strong> the fusion strategies (Table 3.9) do not perform better<br />
than original image. It was also observed that A1 is the best performing subband on<br />
testing set for Yale database. In our approach, four criteria used for subband selection<br />
provide accuracies in the range [91.67%-95.00%] (refer to first row <strong>of</strong> Table 3.3) while<br />
decision fusion using three (sum, product and max) rules in Ekenel’s multiresolution<br />
face recognition provide a PRA <strong>of</strong> only 70% (Table 3.9). We are however able to<br />
obtain a 100% recognition in case <strong>of</strong> Yale database (see last row <strong>of</strong> Table 3.9), using<br />
fourth criterion C4 <strong>of</strong> subband selection and LDA (see second row <strong>of</strong> Table 3.3).<br />
For PIE database, we obtain comparable performance with Ekenel’s multireso-<br />
lution technique. PRAs for the best performing subband (HA3), decision fusion for<br />
sum and product rules are 90.64%, 85.83% and 88.20% respectively, while our subject-<br />
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