Master Thesis - Department of Computer Science

Table 4.6: Performance of Dual Space Face Recognition on Yale, ORL and PIE
databases. PLDA and PNLDA are our proposed decision fusion techniques, using LDA
and nonparametric LDA respectively. BS: Backward Selection; FS: Forward Selection.
Feature Fusion Decision Fusion
Covariance Sum Gramm-Schmidt Existing Techniques Proposed Methods
Databases BS FS BS FS Sum Product PLDA PNLDA
Yale 85.00 85.00 85.00 85.00 86.67 86.67 86.67 86.67
ORL 85.83 85.93 85.83 85.99 81.67 81.67 96.67 97.50
PIE 88.40 88.40 87.28 87.35 83.72 83.85 100.00 100.00
4.6 Conclusion
In this chapter, a dual space based face recognition approach using feature and deci-
sion fusion from null space and range space of within-class scatter is proposed. The
discriminative directions spread over the total face space. Null space and range space
of with-class scatter constitutes the complete face space. So finding discriminative
direction only in null space ignores the utilization of discriminative directions present
in range space. We tried to merge the discriminative directions available in both
spaces using covariance sum and Gramm-Schmidt Orthonormalization method. We
also combine the complementary discriminatory information present in null space and
range space by decision fusion. Along with sum and product rule for decision fusion
we propose a new technique which learns both classifier’s (built on null space and
range space) behavior to enhance class separability (using LDA and nonparametric
LDA) at decision level using classifier’s response as training information. Experimen-
tal results on three standard databases show that, feature and decision fusion indeed
combines information from both spaces to improve classification performance.
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