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Auflistung nach Autor:in "Ramachandra,Raghavendra"

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  • Textdokument
    Biometric Systems under Morphing Attacks: Assessment of Morphing Techniques and Vulnerability Reporting
    (BIOSIG 2017, 2017) Scherhag,Ulrich; Nautsch,Andreas; Rathgeb,Christian; Gomez-Barrero,Marta; Veldhuis,Raymond N.J.; Spreeuwers,Luuk; Schils,Maikel; Maltoni,Davide; Grother,Patrick; Marcel,Sébastien; Breithaupt,Ralph; Ramachandra,Raghavendra; Busch,Christoph
    With the widespread deployment of biometric recognition systems, the interest in attacking these systems is increasing. One of the easiest ways to circumvent a biometric recognition system are so-called presentation attacks, in which artefacts are presented to the sensor to either impersonate another subject or avoid being recognised. In the recent past, the vulnerabilities of biometric systems to so-called morphing attacks have been unveiled. In such attacks, biometric samples of multiple subjects are merged in the signal or feature domain, in order to allow a successful verification of all contributing subjects against the morphed identity. Being a recent area of research, there is to date no standardised manner to evaluate the vulnerability of biometric systems to these attacks. Hence, it is not yet possible to establish a common benchmark between different morph detection algorithms. In this paper, we tackle this issue proposing new metrics for vulnerability reporting, which build upon our joint experience in researching this challenging attack scenario. In addition, recommendations on the assessment of morphing techniques and morphing detection metrics are given.
  • Textdokument
    Fusing Biometric Scores using Subjective Logic for Gait Recognition on Smartphone
    (BIOSIG 2017, 2017) Wasnik,Pankaj; Schäfer,Kirstina; Raja,Kiran; Ramachandra,Raghavendra; Busch,Christoph
    The performance of a biometric system gets affected by various types of errors such as systematic errors, random errors, etc. These kinds of errors usually occur due to the natural variations in the biometric traits of subjects, different testing, and comparison methodologies. Neither of these errors can be easily quantifiable by mathematical formulas. This behavior introduces an uncertainty in the biometric verification or identification scores. The combination of comparison scores from different comparators or combination of multiple biometric modalities could be a better approach for improving the overall recognition performance of a biometric system. In this paper, we propose a method for combining such scores from multiple comparators using Subjective Logic (SL), as it takes uncertainty into account while performing to biometric fusion. This paper proposes a framework for a smartphone based gait recognition system with application of SL for biometric data fusion.