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Improving Handwritten Signature-Based Identity Prediction through the Integration of Fuzzy Soft-Biometric Data

da Costa-Abreu, Marjory, Fairhurst, Michael (2012) Improving Handwritten Signature-Based Identity Prediction through the Integration of Fuzzy Soft-Biometric Data. In: Frontiers in Handwriting Recognition (ICFHR), 2012 International Conference, 18-20 Sept. 2012, Bari. (doi:10.1109/ICFHR.2012.221) (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) (KAR id:35913)

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.
Official URL:
http://dx.doi.org/10.1109/ICFHR.2012.221

Abstract

Automated identification of individuals using biometric technologies is finding increasing application in diverse areas, yet designing practical systems can still present significant challenges. Choice of the modality to adopt, the classification/matching techniques best suited to the application, the most effective sensors to use, and so on, are all important considerations, and can help to ameliorate factors which might detract from optimal performance. Less well researched, however, is how to optimise performance by means of exploiting broader-based information often available in a specific task and, in particular, the exploitation of so-called "soft" biometric data is often overlooked. This paper proposes a novel approach to the integration of soft biometric data into an effective processing structure for an identification task by adopting a fuzzy representation of information which is inherently continuous, using subject age as a typical example. Our results show this to be a promising methodology with possible benefits in a number of potentially difficult practical scenarios.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/ICFHR.2012.221
Subjects: T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Tina Thompson
Date Deposited: 01 Nov 2013 12:14 UTC
Last Modified: 16 Nov 2021 10:12 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/35913 (The current URI for this page, for reference purposes)

University of Kent Author Information

da Costa-Abreu, Marjory.

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CReDIT Contributor Roles:

Fairhurst, Michael.

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