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Auflistung nach Autor:in "Haryanto, Nathania E."

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  • Konferenzbeitrag
    Impact of Doppelgängers on Face Recognition: Database and Evaluation
    (BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Rathgeb, Christian; Drozdowski, Pawel; Obel, Marcel; Dörsch, André; Stockhardt, Fabian; Haryanto, Nathania E.; Bernardo, Kevin; Busch, Christoph
    Look-alikes, a.k.a. doppelgängers, increase the probability of false matches in a facial recognition system, in contrast to random face image pairs selected for non-mated comparison trials. In order to analyse and improve the robustness of automated face recognition, datasets of doppelgänger face image pairs are needed. In this work, we present a new face database consisting of 400 pairs of doppelgänger images. Subsequently, two state-of-the-art face recognition systems are evaluated on said database and other public datasets, including the Disguised Faces in The Wild (DFW) database. It is found that the collected image pairs yield very high similarity scores resulting in a significant increase of false match rates. To facilitate reproducible research and future experiments in this field, the dataset is made available.
  • Konferenzbeitrag
    Simulation of Print-Scan Transformations for Face Images based on Conditional Adversarial Networks
    (BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Mitkovski, Aleksandar; Merkle, Johannes; Rathgeb, Christian; Tams, Benjamin; Bernardo, Kevin; Haryanto, Nathania E.; Busch, Christoph
    In many countries, printing and scanning of face images is frequently performed as part of the issuance process of electronic travel documents, e.g., ePassports. Image alterations induced by such print-scan transformations may negatively effect the performance of various biometric subsystems, in particular image manipulation detection. Consequently, according training data is needed in order to achieve robustness towards said transformations. However, manual printing and scanning is time-consuming and costly. In this work, we propose a simulation of print-scan transformations for face images based on a Conditional Generative Adversarial Network (cGAN). To this end, subsets of two public face databases are manually printed and scanned using different printer-scanner combinations. A cGAN is then trained to perform an image-to-image translation which simulates the corresponding print-scan transformations. The goodness of simulation is evaluated with respect to image quality, biometric sample quality and performance, as well as human assessment.