BIOMETRY - MULTIMODAL BIOMETRICS AND ITS CHARACTERISTICS , POSITIVE AND NEGATIVE IDENTIFICATION

1. UNIT 2
BIOMETRICS
Aparna Ajayan
Asst Prof
Forensic science and
criminology Department
RVS COLLEGE

2. Multimodal Biometrics
• Multimodal biometrics involve using multiple sources of data, like
fingerprints, facial features, iris scans, voice recognition, etc., to
enhance security and accuracy in identity verification.
• This approach is more reliable than using a single biometric modality,
as it combines different traits to reduce the risk of false positives or
negatives.
• Multimodal biometric authentication combines two or more
identifiers, such as fingerprint, face, or iris, to either enhance the user-
experience or boost the security (or both!) of user authentication.

3. • A multi-modal system combining fingerprint and finger vein
characteristics for biometric recognition would be considered a “multi-
modal” system regardless of whether fingerprint and finger vein
images were captured by different or the same biometric devices.

4. Characteristics of multimodal biometrics
• Enhanced Accuracy: Multimodal biometrics leverages the strengths of multiple
biometric modalities, reducing the risk of false acceptances or rejections and
increasing overall accuracy.
• Robustness: Combining different biometric traits compensates for individual
variations, environmental factors, and changes over time, leading to more reliable
identification.
• Security: The fusion of multiple biometric traits makes it significantly harder for
impostors to mimic or spoof the system, enhancing overall security.
• Privacy: Unlike unimodal biometrics, which might require the collection of a
single sensitive biometric, multimodal systems can be designed to use non-
sensitive traits, preserving user privacy
• .Flexibility: Multimodal systems can adapt to various scenarios and user
preferences, allowing for a tailored and user-friendly experience

5. • Integration: These systems can be integrated with existing security
systems or databases, making them suitable for a wide range of
applications, from access control to digital payments.
• Challenges: Implementing multimodal biometrics requires addressing
challenges like data fusion techniques, interoperability, and hardware
compatibility.
• Performance Trade-offs: While offering enhanced accuracy,
multimodal systems may involve increased computational complexity
and cost due to the need for multiple sensors or data processing
methods.
• Continuous Authentication: Multimodal systems can provide
continuous authentication by constantly monitoring and validating
multiple biometric traits, enhancing security in real-time.

6. • Adaptability: Multimodal systems can adapt to changing conditions
or injuries that might affect one biometric modality, ensuring reliable
identification.
• Usability: Users might find multimodal biometrics more convenient
as they can choose from multiple methods based on context or
preference.
• Ethical Considerations: Implementing multimodal biometrics
requires addressing potential ethical concerns related to privacy,
consent, and potential misuse of sensitive data.

7. Advantage
• Enhanced Accuracy: Combining multiple biometric modalities improves the
accuracy of identification and reduces the likelihood of false positives and false
negatives.
• Increased Security: Multimodal biometrics provide a higher level of security by
making it difficult for attackers to simultaneously spoof multiple biometric traits.
• Robustness: The system remains reliable even in situations where one biometric
modality might fail due to injuries, changes over time, or environmental
conditions.
• Reduced Vulnerability to Spoofing: It becomes more challenging for attackers to
successfully mimic or spoof multiple biometric traits, enhancing overall system
security.
• User-Friendly: Users have the flexibility to choose the biometric modality that
suits their preferences, making the system more user-friendly. Privacy:
Multimodal systems can be designed to use non-sensitive biometric traits,
preserving user privacy while still maintaining security.

8. Disadvantage
• Complexity and Cost: Implementing multimodal biometrics can be
more complex and costly due to the need for multiple sensors,
hardware, and data processing techniques.
• Integration Challenges: Integrating multiple biometric modalities
into existing systems might require additional effort and resources.
• Performance Trade-offs: The enhanced accuracy comes at the cost of
increased computational complexity, potentially leading to slower
response times.
• Ethical Concerns: Collecting and storing multiple biometric traits
raises ethical concerns related to user consent, privacy, and potential
misuse of sensitive data.

9. • User Acceptance: Some users might find the use of multiple biometric
modalities cumbersome or overwhelming, impacting user acceptance.
• Data Storage and Management: Storing and managing data from
multiple biometric traits requires effective data security measures to
prevent breaches.
• Error Propagation: If one of the biometric modalities has errors or
inaccuracies, they can potentially affect the overall accuracy of the
system.

10. Key Biometric Process
Refer unit 1

11. Positive And Negative Identification
Positive Identification:
• Positive identification, also known as one-to-one identification or verification, is
the process of confirming the identity of an individual by comparing their biometric
data with a previously enrolled template.
• In positive identification, a person claims an identity (provides an ID, username,
etc.), and their biometric data is compared against a stored template associated with
that identity.
• If the biometric data matches the template within an acceptable margin of error, the
individual's identity is verified positively.
• This process is commonly used in applications like unlocking smartphones or
accessing secure systems using fingerprint, facial recognition, or other biometric
data.

12. Negative Identification
• Negative identification, also referred to as one-to-many identification
or identification searching, involves searching a biometric database to
determine if an individual's biometric data matches any of the
templates stored in the database.
• Unlike positive identification, where the person provides their identity
and the system verifies it, negative identification is used when the
identity is unknown, and the goal is to find a match within a database
of templates.
• This is commonly used in law enforcement scenarios where a forensic
sample (like a fingerprint left at a crime scene) is compared against a
database to identify potential suspects.