Investigating the impact of combining handwritten signature and keyboard keystroke dynamics for gender prediction

CAVALCANTE BANDEIRA, D.R., DE PAULA CANUTO, A.M., DA COSTA ABREU, Marjory, FAIRHURST, M., LI, C. and NASCIMENTO, D.S.C. (2019). Investigating the impact of combining handwritten signature and keyboard keystroke dynamics for gender prediction. In: 2019 8th Brazilian Conference on Intelligent Systems (BRACIS). IEEE, 126-131.

[img]
Preview
PDF
BRACIS2019-danilo.pdf - Accepted Version
All rights reserved.

Download (189kB) | Preview
Link to published version:: https://doi.org/10.1109/BRACIS.2019.00031
Related URLs:

    Abstract

    © 2019 IEEE. The use of soft-biometric data as an auxiliary tool on user identification is already well known. Gender, handorientation and emotional state are some examples which can be called soft-biometrics. These soft-biometric data can be predicted directly from the biometric templates. It is very common to find researches using physiological modalities for soft-biometric prediction, but behavioural biometric is often not well explored for this context. Among the behavioural biometric modalities, keystroke dynamics and handwriting signature have been widely explored for user identification, including some soft-biometric predictions. However, in these modalities, the soft-biometric prediction is usually done in an individual way. In order to fill this space, this study aims to investigate whether the combination of those two biometric modalities can impact the performance of a soft-biometric data, gender prediction. The main aim is to assess the impact of combining data from two different biometric sources in gender prediction. Our findings indicated gains in terms of performance for gender prediction when combining these two biometric modalities, when compared to the individual ones.

    Item Type: Book Section
    Additional Information: Electronic ISSN: 2643-6264 Conference Location: Salvador, Brazil, Brazil
    Identification Number: https://doi.org/10.1109/BRACIS.2019.00031
    Page Range: 126-131
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 07 Apr 2020 13:25
    Last Modified: 08 Apr 2020 08:29
    URI: http://shura.shu.ac.uk/id/eprint/25885

    Actions (login required)

    View Item View Item

    Downloads

    Downloads per month over past year

    View more statistics