Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
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CHAPTER 4<br />
Dual Space Face Recognition using<br />
Feature and Decision Fusion<br />
We propose a new face recognition technique by combining information from null<br />
space and range space <strong>of</strong> within-class scatter <strong>of</strong> a face space. In our technique, the<br />
combination <strong>of</strong> information is attempted in two different levels: (i) Feature level and<br />
(ii) Decision level. The combination <strong>of</strong> information at feature level poses a problem<br />
<strong>of</strong> optimally merging two eigenmodels obtained separately from null space and range<br />
space. We use two different methods: 1) Covariance Sum and 2) Gramm-Schmidt<br />
Orthonormalization to construct a new combined space, named as dual space, by<br />
merging two different set <strong>of</strong> discriminatory directions obtained separately from null<br />
space and range space. We employ forward and backward selection techniques to<br />
select the best set <strong>of</strong> discriminative features from dual space and use them for face<br />
recognition.<br />
Combining information at decision level requires the construction <strong>of</strong> classifiers in-<br />
dividually on null space and range space. Then these two classifiers are combined us-<br />
ing three decision fusion strategies. Along with two classical decision fusion strategies<br />
sum rule and product rule, we employ our own decision fusion technique which ex-<br />
ploits each classifier space separately to enhance combined performance. Our method<br />
<strong>of</strong> decision fusion uses Linear Discriminant Analysis (LDA) and nonparametric LDA<br />
on classifier’s response to enhance class separability at classifier output space. Exper-<br />
imental results on three public databases, Yale, ORL and PIE will show the superi-<br />
ority <strong>of</strong> our method over a face recognition technique called Discriminative Common<br />
Vectors (DCV) [20], which is based only on the null space <strong>of</strong> within-class scatter.<br />
Rest <strong>of</strong> the chapter is organized as follows. Section 4.1 provides a brief review<br />
on existing subspace methods for face recognition followed by a concise introduction