Single-sensor hand and footprint-based multimodal biometric ...

2.6 Multimodal biometric systems
2.6 Multimodal biometric systems
In its most demanding forms of application, namely screening (i.e. matching against a
database of wanted persons, e.g. terrorists) and large scale identification (i.e. identification
from a large number of possible subjects, e.g. criminal investigation), biometrics is faced
with the problem of having to guarantee extremely low error rates. Jain et al. [11]
quantified accuracy requirements for matchers as less than 1 · 10 −3 % FNMR for large scale
identification from 1 million members and less than 1% FNMR for screening from a watch
list of 500 members respectively at 1·10 −4 % FMR. The main reason for these requirements
is the fact that in identification mode the FMR is approximately linear dependent on the
number of enroled members in the system database [1]. These rates, however, can hardly
be accomplished in unimodal systems and it is even hard to bridge the gap between current
matchers and performance requirements in multimodal biometric systems. Design issues
of multibiometric systems (as introduced in the last 10 years) are discussed in [32, 21, 33].
Multibiometric techniques were introduced by multi-classifier literature (multibiometric
systems may be seen as multi-classifiers over a two-class classification problem [21]) and
have gained enormous popularity in the last decade due to [33]:
• their ability to improve matching accuracy;
• higher flexibility in case of failure to acquire single biometrics and;
• more difficult biometric system attacks (all individual biometrics have to be attacked
at the same time).
Most multibiometric systems today incorporate fusion in multiple unit and multiple biometrics
scenarios (see [14]), since these combine completely independent pieces of information
and thus result in higher matching improvements [21]. The systems introduced
in this work are single-sensor multibiometric systems and are thus, in the sense of [14],
“only” multiple matcher scenarios (and therefore considered to combine strongly correlated
measurements in the opinion of [21]). However, features are expected to be largely
independent, when extracted at different resolutions, such as e.g. the global (singular
points), local (minutiae) and very-fine (sweat pores) fingerprint levels, and from different
parts of the input image. Using the latter, one can see that multiple unit scenarios in
[14] may be considered as subsets of multiple matcher scenarios when the input covers
multiple units and is constrained in size for single matchers. This is, in fact, the case in
the proposed system, when fingerprint regions of single fingers are extracted as part of the
preprocessing step and the results of individual units are merged.
Multimodal biometric systems employ fusion strategies to consolidate information. According
to [32] fusion may be incorporated:
• at the feature extraction level consolidating multiple independent biometric
feature vectors via concatenation into one single high-dimensional template;
• at the matching score level combining the individual scores of multiple matchers
into one score indicating the similarity between feature vector and reference template;
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