Master Thesis - Department of Computer Science

5.1 Introduction
In recent years, the concept of combining multiple experts [60],[68] in a unified frame-
work to generate a robust decision based on the individual decisions delivered by mul-
tiple co-operating experts has been examined in the context of biometry [115],[22].
Unimodal biometrics have certain limitations like nonuniversality, spoof attacks, sen-
sitivity to noisy data. This causes unimodal (e.g. face-based, fingerprint-based sys-
tems) recognition techniques to reach a near saturation stage in terms of performance
in future. A potential way of overcoming such limitations consists in combining mul-
tiple modalities. This solution usually called as multimodal biometrics has already
shown good promise. We have chosen face and fingerprint for their strong universality
and uniqueness property respectively.
Generally, decision fusion in multimodal biometrics can be treated as a Multiple
Classifier Combination (MCC) problem. The success and failure of a decision com-
bination strategy largely depends on the extent to which the various possible sources
of information are exploited from each classifier space in designing the decision com-
bination framework. Kittler et al. [60] proposed a set of decision fusion strategies
namely, sum, product, min, max, median and majority vote rule. The paper states
that the ensemble of classifiers providing decisions in the form of crisp class labels
can use majority voting, whereas if classifier outputs are in the form of posterior
probabilities they can be combined using sum, product, max, min rules. Snelick et
al. [115] has used classifier specific weight (MW: Matcher Weight) and class specific
weight (UW: User Weight) with sum rule to combine face and fingerprint at a large
scale. Besides these techniques, researchers have suggested decision-level data fusion.
Several modalities like still image and speech are combined by using fuzzy k-means
(FKM), fuzzy vector quantization (FVQ) algorithms and median radial basis function
(MRBF) network [22]. Kuncheva et al. [68] has proposed decision templates (DT)
for multiple classifier fusion. Per class DT’s are estimated on a validation set and
are then matched to the decision profiles (DP) of new incoming test samples by some
similarity measure. Dempster-Shafer (DS) combination [103] finds out belief degrees
for every class as well as for every classifier (for a test sample), and multiply (as per
product rule) them to give a combined soft class label vector.
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