Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
You also want an ePaper? Increase the reach of your titles
YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.
Table 3.5: Peak Recognition Accuracy (PRA) and EER <strong>of</strong> subband face representa-<br />
tion integrated with PCA, LDA, 2D-PCA, 2D-LDA and DCV with subject-specific<br />
subbands obtained using four criteria on ORL database.<br />
Subband Face(C1) Subband Face(C2) Subband Face(C3) Subband Face(C4)<br />
Methods PRA EER PRA EER PRA EER PRA EER<br />
PCA 88.33 4.83 88.33 4.64 88.33 4.64 81.67 7.50<br />
LDA 88.33 4.16 87.50 4.12 87.50 4.12 89.17 4.16<br />
2D-PCA 81.67 7.50 81.67 7.50 81.67 7.50 81.67 7.50<br />
2D-LDA 91.67 1.67 91.67 1.81 91.67 2.50 90.00 1.98<br />
DCV 85.83 3.44 86.67 3.50 87.50 3.33 88.33 3.59<br />
distance metrics namely, the L1 norm, the L2 norm and the normalized correlation<br />
coefficient. In two separate sets <strong>of</strong> experiments they searched for the subbands insen-<br />
sitive to expression and illumination. They report that very less number <strong>of</strong> subbands<br />
(A1, A2, A3) hold expression invariant information. The same comment does not hold<br />
for illumination invariation. In both cases, all such subbands performing equally good<br />
or better (compared to original face) were selected for further processing using data,<br />
feature and decision level fusion. In decision fusion they used sum, product and max<br />
rules.<br />
We have experimented their technique using PCA, in two different ways. In the<br />
first case, we follow their implementation <strong>of</strong> selecting successful subbands based on<br />
the results obtained on testing set. This is however not technically sound in the sense<br />
that, selected successful subbands are over-tuned on the testing set and will obviously<br />
produce the best possible result only on the testing set. So the right way <strong>of</strong> selecting<br />
successful subbands is to evaluate them on a validation set (as given in Section 3.4.2)<br />
and then combine them to verify the performance on the testing set <strong>of</strong> a database.<br />
This is the second approach used for comparing the performance <strong>of</strong> our proposed<br />
approach with that <strong>of</strong> [36].<br />
For the first case, Table 3.6 shows the best performing (successful) subbands<br />
selected using the testing set. Table 3.7 shows the performance <strong>of</strong> Ekenel’s multireso-<br />
65