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Auflistung nach Autor:in "Wasnik, Pankaj"

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  • Konferenzbeitrag
    Fake Face Detection Methods: Can They Be Generalized?
    (BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Khodabakhsh, Ali; Ramachandra, Raghavendra; Raja, Kiran; Wasnik, Pankaj; Busch, Christoph
    With advancements in technology, it is now possible to create representations of human faces in a seamless manner for fake media, leveraging the large-scale availability of videos. These fake faces can be used to conduct personation attacks on the targeted subjects. Availability of open source software and a variety of commercial applications provides an opportunity to generate fake videos of a particular target subject in a number of ways. In this article, we evaluate the generalizability of the fake face detection methods through a series of studies to benchmark the detection accuracy. To this extent, we have collected a new database of more than 53;000 images, from 150 videos, originating from multiple sources of digitally generated fakes including Computer Graphics Image (CGI) generation and many tampering based approaches. In addition, we have also included images (with more than 3;200) from the predominantly used Swap-Face application that is commonly available on smart-phones. Extensive experiments are carried out using both texture-based handcrafted detection methods and deep learning based detection methods to find the suitability of detection methods. Through the set of evaluation, we attempt to answer if the current fake face detection methods can be generalizable.
  • Konferenzbeitrag
    Improved Fingerphoto Verification System Using Multi-scale Second Order Local Structures
    (BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Wasnik, Pankaj; Ramachandra, Raghavendra; Stokkenes, Martin; Raja, Kiran; Busch, Christoph
    Today’s high-end smartphones are embedded with advanced fingerprint biometric recognition systems that require dedicated sensors to capture the fingerprint data. The inclusion of such sensors helps in achieving better biometric performance and hence can enable various applications that demand reliable identity verification. However, fingerphoto recognition systems have some inherent advantages over fingerprint recognition such as no latent fingerprints, and it enables the possibility to capture multiple samples at once from a biometric instance with minimal user interaction. Thus, user authentication based on fingerphotos could be a useful alternative as we can re-use the smartphone camera to capture the fingerphotos. On the other hand, such an approach introduces different challenges; for example illumination, orientation, background variation, and focus resulting in lower biometric performance. In this research, we propose a novel verification framework based on the feature extracted from the eigenvalues of convolved images using multi-scale second order Gaussian derivatives. The proposed framework is used to authenticate individuals based on images/ videos of their fingers captured using the built-in smartphone cameras. When combining with the commercial off the shelf (COTS) system, the proposed feature extraction technique has achieved the improved verification performance with an equal error rate of 2:76%.