Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
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Table 3.1: Sample distribution (per subject) in training, validation and testing sets<br />
for Yale, PIE and ORL databases.<br />
Set Yale PIE ORL<br />
Training 4 4 3<br />
Validation 3 12 4<br />
Testing 4 26 3<br />
Table 3.2: Peak Recognition Accuracy (PRA) and EER <strong>of</strong> original gray-level face im-<br />
age with PCA, LDA, 2D-PCA, 2D-LDA and DCV for Yale, PIE and ORL databases.<br />
Yale PIE ORL<br />
Methods PRA EER PRA EER PRA EER<br />
PCA 81.67 7.78 58.85 17.05 80.83 3.33<br />
LDA 85.00 6.67 88.14 2.39 79.17 4.21<br />
2D-PCA 85.00 5.44 75.96 7.56 80.83 3.33<br />
2D-LDA 81.67 5.00 83.59 4.10 86.67 2.31<br />
DCV 81.67 5.71 85.90 3.29 82.50 5.00<br />
is applicable for the other three columns <strong>of</strong> the table. For five subspace methods<br />
namely, PCA, LDA 2D-PCA, 2D-LDA and DCV, we obtained maximum accuracies<br />
<strong>of</strong> 95.00%, 100%, 98.33%, 93.33% and 95.00%, respectively. In case <strong>of</strong> EER, LDA<br />
provides a minimum value <strong>of</strong> 0.47% using the fourth criterion for selecting subbands.<br />
Among others, both PCA and DCV are observed to provide EER <strong>of</strong> 3.33% for second<br />
criterion. We gained a maximum accuracy <strong>of</strong> 15% over original face in case <strong>of</strong> LDA<br />
when tested with the fourth criterion.<br />
The performance on PIE database is tabulated in Table 3.4. All criteria provide<br />
same PRA and EER <strong>of</strong> 85.38% and 4.51%, respectively for PCA. DCV produces the<br />
maximum PRA <strong>of</strong> 95.32% and minimum EER <strong>of</strong> 1.13% using the third criterion.<br />
Over all subspace methods, the maximum gain in PRA is obtained as 26.53% for<br />
PCA, for all the criteria.<br />
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