Dynamic security management in multibiometrics

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic researchpeer-review

1 Citation (Scopus)

Abstract

Introduction Biometrics-based personal identification systems offer automated or semiautomated solutions to various aspects of security management problems. These systems ensure controlled access to the protected resources and provide higher security and convenience to the users. The security of the protected resources and information can be further enhanced with the usage of multibiometrics systems. The multibiometric systems are known to offer enhanced security and antispoofing capabilities while achieving higher performance. These systems can utilize multiple biometric modalities, multiple biometric samples, multiple classifiers, multiple features, and/or normalization schemes to achieve performance improvement (refer to chapter x for more details). However, the higher security and reliability offered by multibiometrics systems often come with additional computational requirements and user inconvenience, which can include privacy and hygienic concerns. Therefore the deployment of multibiometrics systems for civilian and commercial applications is often a judicious compromise between these conflicting requirements. The management of multibiometric systems to adaptively ensure the varying level of security requirements, user convenience, and constraints has invited very little attention in the literature. Very little work has been done on the theory, architecture, implementation, or performance estimation of multibiometrics that dynamically ensure the varying level of security requirements. Why Dynamic Security Management? The expected security requirements from the multibiometrics systems are typically expressed in terms of error rates and reliability of the employed system. These error rates correspond to false acceptance rate (FAR), which is the rate at which imposters are accepted as genuine users, or false rejection rate (FRR), which is the rate at which genuine users are rejected by the system as imposters.
Original languageEnglish
Title of host publicationMultibiometrics for Human Identification
PublisherCambridge University Press
Pages302-320
Number of pages19
Volume9780521115964
ISBN (Electronic)9780511921056
ISBN (Print)9780521115964
DOIs
Publication statusPublished - 1 Jan 2011

ASJC Scopus subject areas

  • Computer Science(all)

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