Master Thesis - Department of Computer Science

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