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Title: Extending the predictive capabilities of hand-oriented behavioural biometric systems
Author: Li, Cheng
Awarding Body: University of Kent
Current Institution: University of Kent
Date of Award: 2016
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The discipline of biometrics may be broadly defined as the study of using metrics related to human characteristics as a basis for individual identification and authentication, and many approaches have been implemented in recent years for many different scenarios. A sub-section of biometrics, specifically known as soft biometrics, has also been developing rapidly, which focuses on the additional use of information which is characteristic of a user but not unique to one person, examples including subject age or gender. Other than its established value in identification and authentication tasks, such useful user information can also be predicted within soft biometrics modalities. Furthermore, some most recent investigations have demonstrated a demand for utilising these biometric modalities to extract even higher-level user information, such as a subject\textsc{\char13}s mental or emotional state. The study reported in this thesis will focus on investigating two soft biometrics modalities, namely keystroke dynamics and handwriting biometrics (both examples of hand-based biometrics, but with differing characteristics). The study primarily investigates the extent to which these modalities can be used to predict human emotions. A rigorously designed data capture protocol is described and a large and entirely new database is thereby collected, significantly expanding the scale of the databases available for this type of study compared to those reported in the literature. A systematic study of the predictive performance achievable using the data acquired is presented. The core analysis of this study, which is to further explore of the predictive capability of both handwriting and keystroke data, confirm that both modalities have the capability for predicting higher level mental states of individuals. This study also presents the implementation of detailed experiments to investigate in detail some key issues (such as amount of data available, availability of different feature types, and the way ground truth labelling is established) which can enhance the robustness of this higher level state prediction technique.
Supervisor: Fairhurst, Michael Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available