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Auflistung nach Autor:in "Arakala, Arathi"

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  • Konferenzbeitrag
    Application of affine-based reconstruction to retinal point patterns
    (BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Sadeghpour, Mahshid; Arakala, Arathi; Davis, Stephen A.; Horadam, Kathy J.
    Inverse biometrics that exploit the information of biometric references from comparison scores can compromise sensitive personal information of the users in biometric recognition systems. One inverse biometric method that has been very successful in regenerating face images applies an affine transformation to model the face recognition algorithm. This method is general and could apply to templates extracted from other biometric characteristics. This research proposes two formats to apply this method to spatial point patterns extracted from retina images and tests its performance on reconstructing such sparse templates. The results show that the quality of the reconstructed retina point pattern templates is lower than would be accepted by the system as mated.
  • Konferenzbeitrag
    DEFT: A new distance-based feature set for keystroke dynamics
    (BIOSIG 2023, 2023) Kaluarachchi, Nuwan; Kandanaarachchi, Sevvandi; Moore, Kristen; Arakala, Arathi
    Keystroke dynamics is a behavioural biometric utilised for user identification and authentication. We propose a new set of features based on the distance between keys on the keyboard, a concept that has not been considered before in keystroke dynamics. We combine flight times, a popular metric, with the distance between keys on the keyboard and call them as Distance Enhanced Flight Time features (DEFT). This novel approach provides comprehensive insights into a person’s typing behaviour, surpassing typing velocity alone. We build a DEFT model by combining DEFT features with other previously used keystroke dynamic features. The DEFT model is designed to be device-agnostic, allowing us to evaluate its effectiveness across three commonly used devices: desktop, mobile, and tablet. The DEFT model outperforms the existing state-of-the-art methods when we evaluate its effectiveness across two datasets. We obtain accuracy rates exceeding 99% and equal error rates below 10% on all three devices.