Publikationen

Autor	Tapia, Juan E.; Nieto, Mario; Espin, Juan M.; Rocamora, Alvaro S.; Barrachina, Javier; Damer, Naser; Busch, Christoph; Ivanovska, Marija; Todorov, Leon; Khizbullin, Renat; Lazarevich, Lazar; Grishin, Aleksei; Schulz, Daniel; Gonzalez, Sebastian; Mohammadi, Amir; Kotwal, Ketan; Marcel, Sebastien; Mudgalgundurao, Raghavendra; Raja, Kiran; Schuch, Patrick; Patwardhan, Sushrut; Ramachandra, Raghavendra; Pereira, Pedro Couto; Pinto, Joao Ribeiro; Xavier, Mariana; Valenzuela, Andrés; Lara, Rodrigo; Batagelj, Borut; Peterlin, Marko; Peer, Peter; Muhammed, Ajnas; Nunes, Diogo; Gonçalves, Nuno
Datum	2025
Art	Conference Paper
Abstrakt	This work summarises and reports the results of the second Presentation Attack Detection competition on ID cards. This new version includes new elements compared to the previous one. (1) An automatic evaluation platform was enabled for automatic benchmarking; (2) Two tracks were proposed in order to evaluate algorithms and datasets respectively; and (3) A new ID card dataset was shared with Track 1 teams to serve as the baseline dataset for the training and optimisation. The Hochschule Darmstadt, Fraunhofer-IGD, and Facephi company jointly organised this challenge. 20 teams were registered, and 74 submitted models were evaluated. For Track 1, the "Dragons" team reached first place with an Average Ranking and Equal Error rate (EER) of (AVRank) of 40.48% and 11.44% EER, respectively. For the more challenging approach in Track 2, the "Incode" team reached the best results with an AVRank of 14.76% and 6.36% EER, improving on the results of the first edition of 74.30% and 21.87% EER, respectively. These results suggest that PAD on ID cards is improving, but it is still a challenging problem related to the number of images, especially of bona fide images.
Konferenz	International Joint Conference on Biometrics 2025
Projekt	Next Generation Biometric Systems
Url	https://publica.fraunhofer.de/handle/publica/511463