Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

FAIRHURST, M., LI, C. and DA COSTA ABREU, Marjory (2017). Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data. IET Biometrics, 6 (6), 369-378.

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Abstract

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future.

Item Type:	Article
Additional Information:	© 2017 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Uncontrolled Keywords:	biometrics (access control); predictive biometrics; personal characteristic prediction; soft biometrics information; person identification; soft biometrics processing; user authentication; biometric data processing
Identification Number:	https://doi.org/10.1049/iet-bmt.2016.0169
Page Range:	369-378
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	07 Jan 2020 15:07
Last Modified:	18 Mar 2021 01:51
URI:	https://shura.shu.ac.uk/id/eprint/25394

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