Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
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Table 4.2: Effect <strong>of</strong> increasing number <strong>of</strong> training samples on the performance <strong>of</strong> null<br />
space and range space for ORL database.<br />
No. <strong>of</strong> Training Null Space Range Space<br />
Samples<br />
2 82.19 55.00<br />
3 85.00 72.86<br />
4 88.75 82.50<br />
5 88.50 85.00<br />
6 91.25 90.63<br />
7 93.33 93.33<br />
8 95.00 97.50<br />
9 92.00 90.00<br />
Now to summarize the above discussion on the effect <strong>of</strong> increasing number <strong>of</strong><br />
training samples on the performance <strong>of</strong> null space and range space, we state the<br />
following points:<br />
• For Yale database, the optimal performances for null space and range space<br />
occur for relatively lesser number <strong>of</strong> training samples (2/3 samples for null<br />
space and 4 samples for range space).<br />
• For ORL and PIE database, the maximum accuracies for null space and range<br />
space are obtained for relatively larger number <strong>of</strong> training samples (8 and 10<br />
samples for ORL and PIE, respectively).<br />
However the reasons behind the above observations are explained in the following:<br />
• Yale database consists <strong>of</strong> diverse variations in expressions as well as illumi-<br />
nation, and even has face with spectacles. Even though Yale database is the<br />
smallest among the three databases used for experimentation, it provides a<br />
rich combination <strong>of</strong> the challenging factors (expression, illumination and cam-<br />
ouflage) for automatic face recognition, known in practice. Due to the high<br />
diversity among the training samples, the captured common features are not<br />
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