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

da Costa-Abreu, Marjory and Fairhurst, Michael (2012) Improving Handwritten Signature-Based Identity Prediction through the Integration of Fuzzy Soft-Biometric Data. In: 2012 International Conference on Frontiers in Handwriting Recognition. pp. 797-802. (doi:https://doi.org/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)

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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)
Subjects: T Technology
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: Tina Thompson
Date Deposited: 01 Nov 2013 12:14 UTC
Last Modified: 15 May 2014 15:20 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/35913 (The current URI for this page, for reference purposes)
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