Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
Master Thesis - Department of Computer Science
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1.2 Multimodal Biometric System<br />
There are a number <strong>of</strong> problems involved in a unimodal biometric systems such as<br />
non-universality, spo<strong>of</strong> attacks, etc. These drive the research community to think<br />
about more robust and secured biometric system which will be more difficult to fool<br />
than a system based on a single biometric. The first choice is obviously a multimodal<br />
biometric system which uses multiple biometric traits to <strong>of</strong>fer robust decision-making.<br />
The main motivations behind multimodal biometric system are the limitation <strong>of</strong> uni-<br />
modal biometric systems in the following ways:<br />
• Noisy sensed data: Unimodal biometric systems are very much sensitive to<br />
the trait they use. Noisy sensed data affects the performance <strong>of</strong> a system by<br />
getting incorrectly matched with templates in the database. Fingerprint with<br />
scar or voice altered by cold, accumulation <strong>of</strong> dirt or residual fingerprint on<br />
fingerprint sensor can result in noisy data.<br />
• Intra-class variations: “Intra-class variation” can be explained by the fact<br />
that the biometric data acquired during verification will not be identical to<br />
the data used for generating user’s template during enrollment due to various<br />
factors. In case <strong>of</strong> face, slight change in pose or ambient light and use <strong>of</strong><br />
spectacles may cause the face to appear different from its template.<br />
• Lack <strong>of</strong> individuality: Features from same biometric trait from different<br />
individuals can be quite similar. For face biometric the facial features like,<br />
eyes, nose, mouth are in right place for everybody. Moreover, twins or father<br />
and son can be identical in facial appearance due to genetic factors. This lack <strong>of</strong><br />
uniqueness increases the False Acceptance Rate (FAR) <strong>of</strong> a biometric system.<br />
• Non-universality: No biometric is truly universal. For example, fingerprints<br />
are supposedly universal, but there is a small fraction <strong>of</strong> the population that<br />
does not possess fingerprint due to hand-related disabilities that are unsuitable<br />
for automatic matching. Thus, there is a failure to enroll (FTE) rate associated<br />
with using a single biometric trait. It has been empirically estimated that<br />
around 4% <strong>of</strong> fingerprint images have poor quality ridges.<br />
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