Master Thesis - Department of Computer Science

From Tables 4.1-4.3, we conclude the following: A large quantity of training sam-
ples drive discriminative information to the range space of within-class scatter. For
a database with a large number of subjects, the number of training samples will not
allow null space to perform well. The main aim of our work is to capture the dis-
criminative informations available in range space and combine them efficiently with
the discriminative informations obtained from null space. Thus, we exploit the whole
gamut of discriminative informations present in the entire face space and utilize them
for enhancing classification performance.
4.5.2 Performance of Dual Space Face Recognition Approach
We split a image database into three disjoint sets called as training, validation and
testing set. Feature fusion technique requires validation set for selecting optimal
feature, W Dual
opt
and the performance of selected features is observed on the testing
set. For decision fusion, training set along with validation set is required for generating
“training response vector set” that is used for learning each classifier and to construct
LDA or nonparametric LDA based eigenmodel at classifier output space. The sample
distribution for a single subject (which is same for feature fusion and decision fusion)
over the three sets for all three databases are given in Table 4.4.
Table 4.4: Sample distribution (per subject) in training, validation and testing sets
for Yale, ORL and PIE databases.
Set Yale ORL PIE
Training 4 3 4
Validation 3 4 12
Testing 4 3 26
The unique decomposition of a face into null space and range space is displayed
in Fig 4.6, with three faces drawn from Yale, ORL and PIE databases respectively.
Fig. 4.7 shows the images used for training for a single subject on Yale, ORL and
PIE databases. Table 4.5 shows the performance of null and range spaces for Yale,
ORL and PIE databases. The performances of our dual space based methods using
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