Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
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(Original face) (Null face) (Range face)<br />
(Original face) (Null face) (Range face)<br />
(Original face) (Null face) (Range face)<br />
Figure 4.6: Unique decomposition <strong>of</strong> faces into null space and range space <strong>of</strong><br />
within-class scatter drawn from Yale (first row), ORL (second row) and PIE (third<br />
row) databases.<br />
feature (first four columns) and decision fusion (last four columns) are tabulated in<br />
Table 4.6. BS and FS are the abbreviations for Backward Selection and Forward<br />
Selection respectively.<br />
For Yale database, dual space based feature fusion and decision fusion methods<br />
provide 85.00% and 86.67% (see Table 4.6), respectively. We obtain 85.99% accuracy<br />
using feature fusion (Gramm-Schmidt Orthonormalization and forward selection) and<br />
97.50% using decision fusion (PNLDA) for ORL database. Feature fusion using co-<br />
variance sum and decision fusion using both PLDA and PNLDA provide 88.40% and<br />
100% respectively on PIE database. Decision fusion using sum rule and product rule<br />
fails to work better than the null space itself. As we can notice that backward and<br />
forward search works equally well for all databases. Results on three databases show<br />
that there exist a good scope <strong>of</strong> combining information from null and range space.<br />
97