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Investigating and Comparing Multimodal Biometric Techniques

  • Christopher Andrade
  • Sebastian H. von Solms
Part of the The International Federation for Information Processing book series (IFIPAICT, volume 261)

Determining the identity of a person has become vital in today’s world. Emphasis on security has become increasingly more common in the last few decades, not only in Information Technology, but across all industries. One of the main principles of security is that a system only be accessed by a legitimate user. According to the ISO 7498/2 document [1] (an international standard which defines an information security system architecture) there are 5 pillars of information security. These are Identification/Authentication, Confidentiality, Authorization, Integrity and Non Repudiation. The very first line of security in a system is identifying and authenticating a user. This ensures that the user is who he/she claims to be, and allows only authorized individuals to access your system. Technologies have been developed that can automatically recognize a person by his unique physical features. This technology, referred to as ‘biometrics’, allows us to quickly, securely and conveniently identify an individual. Biometrics solutions have already been deployed worldwide, and it is rapidly becoming an acceptable method of identification in the eye of the public. As useful and advanced as unimodal (single biometric sample) biometric technologies are, they have their limits. Some of them aren’t completely accurate; others aren’t as secure and can be easily bypassed. Recently it has been reported to the congress of the U.S.A [2] that about 2 percent of the population in their country do not have a clear enough fingerprint for biometric use, and therefore cannot use their fingerprints for enrollment or verification. This same report recommends using a biometric system with dual (multimodal) biometric inputs, especially for large scale systems, such as airports. In this technical paper we will investigate and compare multimodal biometric techniques, in order to determine how much of an advantage lies in using this technology, over its unimodal equivalent.

Keywords

Normalization Method Fusion Method Biometric System False Acceptance Rate False Rejection Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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Copyright information

© International Federation for Information Processing 2008

Authors and Affiliations

  • Christopher Andrade
    • 1
  • Sebastian H. von Solms
    • 1
  1. 1.Academy for Information TechnologyUniversity of JohannesburgSouth Africa

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