Master Thesis - Department of Computer Science

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