Navegando por Palavras-chave "Template update"

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    Enhanced template update: Application to keystroke dynamics
    (Elsevier Advanced Technology, 2016) Pisani, Paulo Henrique; Giot, Romain; de Carvalho, Andre C. P. L. F.; Lorena, Ana Carolina [UNIFESP]
    With the increasing number of activities being performed using computers, there is an ever growing need for advanced authentication mechanisms like biometrics. One efficient and low cost biometric modality is keystroke dynamics, which attempts to recognize users by their typing rhythm. It has been shown that the biometric features may undergo changes over time, which can reduce the predictive performance of the biometric system. Template update adapts the user model to deal with these changes and, therefore, decreases the predictive performance loss. Most of the studies in the literature only take into account samples classified as genuine to perform adaptation. This paper extends this common approach by proposing an original framework to make use of samples classified as impostors, too. This new approach, named Enhanced Template Update, uses all collected unlabeled samples to support the adaptation process. According to our experimental results, this new approach can improve the predictive performance when compared to current methods depending on the scenario. Some improvements on the visualization of results over time are also proposed during the analysis performed in this study. Although the proposed approach is evaluated on keystroke dynamics, it could also be applied to other biometric modalities. (C) 2016 Elsevier Ltd. All rights reserved.
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    Score normalization applied to adaptive biometric systems
    (Elsevier Advanced Technology, 2017) Pisani, Paulo Henrique; Poh, Norman; de Carvalho, Andre C. P. L. F.; Lorena, Ana Carolina [UNIFESP]
    Biometric authentication systems have certain limitations. Recent studies have shown that biometric features may change over time, which can entail a decrease in recognition performance of the biometric system. An adaptive biometric system addresses this problem by adapting the biometric reference/template over time, thereby tracking the changes automatically. However, the use of these systems usually requires the adoption of a high threshold value to avoid the inclusion of impostor patterns into the genuine biometric reference. In this study, we hypothesize that score normalization procedures, which have been used to improve the recognition performance of biometric systems through a better refinement of their decision, can also improve the overall performance of adaptive systems. With such a normalization, a better threshold choice could also be made, which would then increase the number of genuine samples used for adaptation. To the best of our knowledge, this is the first investigation towards the use of score normalization to enhance adaptive biometric systems dealing with the change of user features over time. Through a systematic experimental design tested on two behavioral biometric traits, the obtained results indeed support our conjecture. Moreover, the experimental results show that the performance gain brought by adaptation can have a higher overall impact than score normalization alone. (C) 2017 Elsevier Ltd. All rights reserved.