A study about the impact of combining keystroke and handwriting dynamics on gender and emotional state prediction

• Autor(a) principal: Bandeira, Danilo Rodrigo Cavalcante
• Data de Publicação: 2020
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFRN
• Texto Completo: https://repositorio.ufrn.br/jspui/handle/123456789/28894
• topic: CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO; Biometrics; Ensembles; Combination; Keystroke; Handwriting

The use of soft biometrics as an auxiliary tool for hard biometrics on user identificationbased systems is already well known. It is not, however, the only use possible for soft biometric data, beyond assist hard biometrics, those modalities can also be the predicted from them. Gender, hand-orientation and emotional state are some examples, which can be called soft biometrics. It is very common in the literature the use of physiological hard biometric modalities for soft biometric prediction, but the behavioral data is often neglected. Two possible behavioral modalities that are not often found in the literature are keystroke and handwriting dynamics, which can be seen used alone to predict the user’s gender and emotional state, but not in any kind of combination scenario. To fill this space, this study aims to investigate whether the combination of those two different biometric modalities can impact the gender and emotional state prediction accuracy. In this sense two combination methods were proposed, the data fusion and the decision fusion, with the decision fusion presenting two variation, the first using mixture of experts and the second using ensembles. The achieved results by the proposed methods were compared to the biometric modalities individually, with a substantially improvement being noticed in most combination scenarios. Lastly, all the presented results were confirmed by the application of statistical tests.