Master Thesis - Department of Computer Science

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