Master Thesis - Department of Computer Science

determined by logistic regression.
• Measurement level: The output of the biometric matchers is a set of possi-
ble matches along with the matching scores. Measurement level output contain
richest information about the input pattern. Here, the scores must be trans-
formed to a common domain to ensure a meaningful combination from different
modalities. As this is the most common approach used for fusion, we discuss
it in more detail in the following section.
2.3.3.3 Measurement level
Measurement level fusion can be approached in two ways: (1) Classification approach
and (2) Combination approach. Wang et al. [128] consider the matching scores
resulting from face and iris recognition modules as a two-dimensional feature vector.
Fisher’s discriminant analysis and a neural network classifier with radial basis function
are then used for classification. Verlinde and Cholet [126] combine the scores from
two face recognition experts and one speaker recognition expert using three classifiers:
k-NN classifier using vector quantization, decision tree based classifier and a classifier
based on a logistic regression model. Chatzis et al. [21] use fuzzy k-means and
fuzzy vector quantization, along with a median radial basis function neural network
classifier for the fusion of the scores obtained from biometric systems based on visual
(facial) and acoustic (vocal) features. Ross and Jain [106] use decision tree and
linear discriminant classifiers for combining the scores of face, fingerprint, and hand-
geometry.
In a traditional multiple classifier system, a feature vector x is classified into one
of the C classes using L classifiers {D1, D2, ......, DL}, each using the feature vectors
xl, l = 1, 2, ..., L, respectively. Measurement-level (also called response vector level)
combination strategies give final decision by fusing the response vectors from multiple
classifiers. Formally, for a feature vector x, response vectors from multiple classifiers
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