Master Thesis - Department of Computer Science

CHAPTER 5
Enhancing Decision Combination of Face
and Fingerprint by Exploitation of
Individual Classifier Space: An approach
to Multimodal Biometry
This chapter presents a new approach to combine decisions from face and fingerprint
classifiers by exploiting the individual classifier space on the basis of availability of
class specific information present in the classifier’s output space. We use the response
vectors on a validation set for enhancing class separability (using linear discriminant
analysis and nonparametric linear discriminant analysis) in the classifier output space
and thereby improving performance of the face classifier. Fingerprint classifier often
does not provide this information due to high sensitivity to minutiae points, pro-
ducing partial matches across subjects. The enhanced face and fingerprint classifiers
are combined using sum rule. We also propose a generalized algorithm for Multiple
Classifier Combination (MCC) based on our approach. Experimental results show
superiority of the proposed method over other already existing fusion techniques like
sum, product, max, min rules, decision template and Dempster-Shafer theory. The
rest of the chapter is organized as follows: Section 5.1 provides brief description on a
few relevant works attempted for decision combination and a succinct introduction to
our proposed method of decision fusion. Section 5.2 gives the theoretical formulation
of our proposed classifier combination technique; Section 5.3 describes our method of
exploitation of base classifier using prior information and then combine them; Sec-
tion 5.4 presents the experimental results to support our methodology and Section 5.5
concludes this chapter. The description of other decision combination strategies used
in our experimentation for comparison are provided in Section 2.3.3.3.