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i-com Band 18 (2019) Heft 3

https://dl.gi.de/handle/20.500.12116/31041
Auflistung nach:

Auflistung i-com Band 18 (2019) Heft 3 nach Autor:in "Alt, Florian"

1 - 2 von 2
  • Zeitschriftenartikel
    Emerging Trends in Usable Security and Privacy
    (i-com: Vol. 18, No. 3, 2019) Alt, Florian; von Zezschwitz, Emanuel
    New technologies are constantly becoming part of our everyday life. At the same time, designers and developers still often do not consider the implications of their design choices on security and privacy. For example, new technologies generate sensitive data, enable access to sensitive data, or can be used in malicious ways. This creates a need to fundamentally rethink the way in which we design new technologies. While some of the related opportunities and challenges have been recognized and are being addressed by the community, there is still a need for a more holistic understanding. In this editorial, we will address this by (1) providing a brief historical overview on the research field of ‘Usable Security and Privacy’; (2) deriving a number of current and future trends; and (3) briefly introducing the articles that are part of this special issue and describing how they relate to the current trends and what researchers and practitioners can learn from them.
  • Zeitschriftenartikel
    VPID: Towards Vein Pattern Identification Using Thermal Imaging
    (i-com: Vol. 18, No. 3, 2019) Faltaous, Sarah; Liebers, Jonathan; Abdelrahman, Yomna; Alt, Florian; Schneegass, Stefan
    Biometric authentication received considerable attention lately. The vein pattern on the back of the hand is a unique biometric that can be measured through thermal imaging. Detecting this pattern provides an implicit approach that can authenticate users while interacting. In this paper, we present the Vein-Identification system, called VPID. It consists of a vein pattern recognition pipeline and an authentication part. We implemented six different vein-based authentication approaches by combining thermal imaging and computer vision algorithms. Through a study, we show that the approaches achieve a low false-acceptance rate (“FAR”) and a low false-rejection rate (“FRR”). Our findings show that the best approach is the Hausdorff distance-difference applied in combination with a Convolutional Neural Networks (CNN) classification of stacked images.