The document discusses the challenges of unimodal biometric systems, including issues such as noisy data, intra-class variation, inter-class similarities, non-universality, and spoofing. It introduces multimodal biometrics, which utilize multiple physical or behavioral characteristics to enhance accuracy and security, and outlines different operational modes (serial, parallel, hierarchical) along with the levels of fusion (feature extraction, matching score, decision level). Additionally, it highlights the advantages of multimodal systems, such as increased security and reduced error rates, as well as their disadvantages including cost and complexity.

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2. Content
 Introduction.
 Multimodal Biometrics.
 Scenarios in a multimodal biometric system.
 Modes of Operation.
 Levels of Fusion.
 Advantages & Disadvantages.
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3. Introduction
Unimodal 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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4. 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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5. 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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6. Content
 Introduction.
 Multimodal Biometrics.
 Scenarios in a multimodal biometric system.
 Modes of Operation.
 Levels of Fusion.
 Advantages & Disadvantages.
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7. 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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8. 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
Multiple
Snapshots
Multiple
Units
Multiple
Biometrics

10. 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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11. Content
 Introduction.
 Multimodal Biometrics.
 Scenarios in a multimodal biometric system.
 Modes of Operation.
 Levels of Fusion.
 Advantages & Disadvantages.
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12. Modes
A multimodal biometric system can operate in one of
three different modes:
 Serial
 Parallel
 Hierarchical
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13. 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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14. Modes
 Parallel mode :
information from multiple traits is used simultaneously
to perform recognition.
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15. Modes
 Hierarchical mode :
individual classifiers are combined in a treelike
structure.
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16. Content
 Introduction.
 Multimodal Biometrics.
 Scenarios in a multimodal biometric system.
 Modes of Operation.
 Levels of Fusion.
 Advantages & Disadvantages.
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17. 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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18. 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

19. 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

20. Decision Level Fusion
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Each biometric system makes its own recognition decision
based on its own feature vector.

21. Content
 Introduction.
 Multimodal Biometrics.
 Scenarios in a multimodal biometric system.
 Modes of Operation.
 Levels of Fusion.
 Advantages & Disadvantages.
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22. 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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23. 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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