Auflistung nach Autor:in "Chiesa, Valeria"
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- KonferenzbeitragAdvanced Face Presentation Attack Detection on Light Field Database(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Chiesa, Valeria; Dugelay, Jean-LucIn the last years several works have been focused on the impact of new sensors on face recognition. A particular interest has been addressed to technologies able to detect the depth of the scene as light field cameras. Together with person identification algorithms, new anti-spoofing methods customized for specific devices have to be investigated. In this paper, a new algorithm for presentation attack detection on light field face database is proposed. While distance between subject and camera is not a relevant information for standard 2D spoofing attacks, it could be important when using 3D cameras. We prove through three experiments that the proposed method based on depth map elaboration outperforms the existent algorithms in presentation attack detection on light field images.
- KonferenzbeitragPROTECT Multimodal DB: fusion evaluation on a novel multimodal biometrics dataset envisaging Border Control(BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Sequeira, Ana F.; Chen, Lulu; Ferryma, James; Galdi, Chiara; Chiesa, Valeria; Dugelay, Jean-Luc; Maik, Patryk; Gmitrowicz, Piotr; Szklarski, Lukasz; Prommegger, Bernhard; Kauba, Christof; Kirchgasser, Simon; Uhl, Andreas; Grudzien, Artur; Kowalski, MarcinThis work presents a novel multimodal database comprising 3D face, 2D face, thermal face, visible iris, finger and hand veins, voice and anthropometrics. This dataset will constitute a valuable resource to the field with its number and variety of biometric traits. Acquired in the context of the EU PROTECT project, the dataset allows several combinations of biometric traits and envisages applications such as border control. Based upon the results of the unimodal data, a fusion scheme was applied to ascertain the recognition potential of combining these biometric traits in a multimodal approach. Due to the variability on the discriminative power of the traits, a leave the n-best out fusion technique was applied to obtain different recognition results.