Mulitmodal Biometric Systems

1
Mulitmodal Biometric Systems

• Munir Bandukwala
• Rajesh Subramanian

2
Introduction

• Biometric systems using single biometrics have to
  contend with
• Noisy data.
• Restricted degrees of freedom.
• Non universality of data.
• Unacceptable error rates.

3
Intoduction-contd

• Multibiometric systems reduce these problems by
  providing multiple evidences of the same entity.
• These systems improve overall performance.
• Multimodal systems also make spoofing difficult.
• By prompting for a random set of biometric
  traits, such systems ensure a built in liveness
  test.

4
Problems involved

• A multimodal system needs a particular fusion
  scheme to integrate the data from multiple
  modalities.
• Several fusion schemes are available in the
  literature
• Sum Rule 1
• Decision trees 1
• Linear Discriminant Analysis 1

5
Problems involved-contd

• Further schemes like
• Learning user specific matching thresholds
• Weighting of individual biometric traits or
• A combination of the above
• also seem to bolster performance 2

6
Problem Statement

• We plan to propose a fusion system that will
  provide an integration scheme for Fingerprint,
  Hand Geometry and Signature.
• We propose to find a scheme providing better
  results.
• The fusion will be at the matching score level.

7
References

• 1 Information Fusion in Biometrics-Arun Ross
  and Anil Jain
• 2Learning User-specific parameters in a
  multibiometric system-Anil K. Jain and Arun Ross