Master Thesis - Department of Computer Science

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