multi modal biometric system
TRANSCRIPT
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Content Introduction.
Multimodal Biometrics.
Scenarios in a multimodal biometric system.
Modes of Operation.
Levels of Fusion.
Advantages & Disadvantages.
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IntroductionUnimodal biometrics has several problems such as:
Noisy data.
Intra class variation.
Inter class similarities.
Non universality.
Spoofing.
which cause this system less accurate and secure.
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Introduction Noisy data : Susceptibility of biometric sensors to noise leads
to inaccurate matching, as noisy data may lead to false rejection.
Intra class variation : The biometric data acquired during
verification will not be identical to the data used for generating template during enrollment for an individual. This is known as intra-class variation. Large intra-class variations increase the False Rejection Rate (FRR) of a biometric system.
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Introduction Interclass similarities : Inter-class similarity refers to the
overlap of feature spaces corresponding to multiple individuals. Large Inter-class similarities increase the False Acceptance Rate (FAR) of a biometric system.
Non universality “ Failure to enroll(FTE) ”: Some persons
cannot provide the required standalone biometric, owing to illness or disabilities.
Spoofing : Unimodal biometrics is vulnerable to spoofing
where the data can be imitated or forged.
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Content Introduction.
Multimodal Biometrics.
Scenarios in a multimodal biometric system.
Modes of Operation.
Levels of Fusion.
Advantages & Disadvantages.
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Multimodal Biometrics Some of the limitations imposed by unimodal biometric
systems can be overcome by using multiple biometric
modalities.
Multimodal biometric systems are those that utilize more
than one physical or behavioural characteristic for
enrolment , verification, or identification.
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Content Introduction.
Multimodal Biometrics.
Scenarios in a multimodal biometric system.
Modes of Operation.
Levels of Fusion.
Advantages & Disadvantages.
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Multimodal Biometrics
Multiple sensors
Multiple Matchers
MultipleSnapshots
MultipleUnits
MultipleBiometrics
Scenarios in a multimodal biometric system Multiple sensors : multiple sensors are used to sense the same biometric
identifier.
Multiple Biometrics : sense different biometric identifiers.
Multiple Units : fingerprints from two or more fingers.
Multiple Snapshots : more than one instance of the same biometric.
Multiple Matching algorithm : combines different representation and matching algorithms.
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Content Introduction.
Multimodal Biometrics.
Scenarios in a multimodal biometric system.
Modes of Operation.
Levels of Fusion.
Advantages & Disadvantages.
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ModesA multimodal biometric system can operate in one of
three different modes:
Serial
Parallel
Hierarchical
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Modes Serial mode :
the output of one biometric trait is typically used to narrow down the number of possible identities before the next trait is used.
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Modes Parallel mode :
information from multiple traits is used simultaneously
to perform recognition.
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Modes Hierarchical mode :
individual classifiers are combined in a treelike structure.
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Content Introduction.
Multimodal Biometrics.
Scenarios in a multimodal biometric system.
Modes of Operation.
Levels of Fusion.
Advantages & Disadvantages.
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Fusion Multimodal biometric systems integrate information
presented by multiple biometric indicators. The information can be consolidated at various levels.
Fusion is divided into three parts.
1. Fusion at the feature extraction level.
2. Fusion at the matching score (confidence or rank) level.
3. Fusion at the decision (abstract label) level.
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Feature Level Fusion
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Combining feature vectors
Fusion at feature level is expected to provide better recognition results but it has also observed that when features of different modalities are compatible with each other then fusion at feature level achieves more accuracy
Matching Score Level Fusion
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Feature vectors are processed separately and individual matching score is found and finally these matching scores are combined to make classification.
One important aspect has to be addressed in the matching score level is the normalization of scores obtained from multiple modalities
Decision Level Fusion
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Each biometric system makes its own recognition decision based on its own feature vector.
Content Introduction.
Multimodal Biometrics.
Scenarios in a multimodal biometric system.
Modes of Operation.
Levels of Fusion.
Advantages & Disadvantages.
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Advantages of Multi-modal Biometrics More Secure : hard to spoof.
More accurate.
Reduce False accept rate (FAR).
Reduce False reject rate (FRR).
Reduce Failure to enrol rate (FTE).
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Disadvantages of Multi-modal Biometrics High cost.
High enrolment time.
High transit times.
Increase system development and complexity.
Reduced Matching Level: if a stronger biometric is used with a weaker biometric, the result is not a stronger combined system. The error rate of the weaker biometric can bring down the overall effectiveness of the system.
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