Enhancing Identity Prediction Using a Novel Approach to Combining Biometric and Soft-Biometric Information

Fairhurst, Michael and da Costa-Abreu, Marjory (2010) Enhancing Identity Prediction Using a Novel Approach to Combining Biometric and Soft-Biometric Information. IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, 41 (5). pp. 599-607. ISSN 1094-6977. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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The effectiveness with which individual identity can be predicted in, for example, an antiterrorist scenario can benefit from seeking a broad base of identity evidence. The issue of improving performance can be addressed in a number of ways, but system configurations based on integrating different information sources (often involving more than one biometric modality) are a widely adopted means of achieving this. This paper presents a new approach to improving identification performance, where both direct biometric samples and “soft-biometric” knowledge are combined. Specifically, however, we propose a strategy based on an intelligent agent-based decision-making process, which predicts both absolute identity and also other individual characteristics from biometric samples, as a basis for a more refined and enhanced overall identification decision based on flexible negotiation among class-related agents.

Item Type: Article
Uncontrolled keywords: Agent, face, fingerprint, fusion, identity prediction, soft-biometric prediction (age and gender)
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics (see also: telecommunications), > TK7880 Applications of electronics (inc industrial & domestic) > TK7882.B56 Biometrics
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: J. Harries
Date Deposited: 11 Mar 2011 12:26
Last Modified: 15 May 2014 15:19
Resource URI: https://kar.kent.ac.uk/id/eprint/27475 (The current URI for this page, for reference purposes)
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