First Competition on Presentation Attack Detection on ID Card

This paper summarises the Competition on Presentation Attack Detection on ID Cards (PAD-IDCard) held at the 2024 International Joint Conference on Biometrics (IJCB 2024). The competition attracted a total of ten registered teams, both from academia and industry. In the end, the participating teams submitted five valid submissions, with eight models to be evaluated by the organisers. The competition presented an independent assessment of current state-of-the-art algorithms. Today, no independent evaluation on cross-dataset is available; therefore, this work determined the state-of-the-art on ID cards. To reach this goal, a sequestered test set and baseline algorithms were used to evaluate and compare all the proposals. The sequestered test dataset contains ID cards from four different countries. In summary, a team that chose to be "Anonymous" reached the best average ranking results of 74.80%, followed very closely by the "IDVC" team with 77.65%.

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Publication Details

Published
2024-09-15
DOI
https://doi.org/10.1109/ijcb62174.2024.10744475
Citations
7
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
2.44
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article

First Competition on Presentation Attack Detection on ID Card

Christoph Busch, Juan Tapia, Borut Batagelj, Naser Damer et al.
7 citations
Network Security and Intrusion Detection
2.44
article

First Competition on Presentation Attack Detection on ID Card

Christoph Busch, Juan Tapia, Borut Batagelj, Naser Damer, Javier Barrachina, Raghavendra Ramachandra, Raghavendra Mudgalgundurao, Juan M. Espín, Kiran Raja, Daniel Schulz, Krištof Ocvirk, L Alessio, Carlos Aravena, Sushrut Patwardhan, Alvaro S. Rocamora
article en
7 citations

Abstract

This paper summarises the Competition on Presentation Attack Detection on ID Cards (PAD-IDCard) held at the 2024 International Joint Conference on Biometrics (IJCB 2024). The competition attracted a total of ten registered teams, both from academia and industry. In the end, the participating teams submitted five valid submissions, with eight models to be evaluated by the organisers. The competition presented an independent assessment of current state-of-the-art algorithms. Today, no independent evaluation on cross-dataset is available; therefore, this work determined the state-of-the-art on ID cards. To reach this goal, a sequestered test set and baseline algorithms were used to evaluate and compare all the proposals. The sequestered test dataset contains ID cards from four different countries. In summary, a team that chose to be "Anonymous" reached the best average ranking results of 74.80%, followed very closely by the "IDVC" team with 77.65%.

Darmstadt University of Applied Sciences (DE), University of Ljubljana (SI), Norwegian University of Science and Technology (NO), Fraunhofer Institute for Computer Graphics Research (DE), Zimmer Biomet (Germany) (DE)
Openalex Percentile: Top 10%
Network Security and Intrusion Detection
2.44
DOIOpen Access PDFOpenAlex
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First Competition on Presentation Attack Detection on ID Card — Christoph Busch, Juan Tapia, et al. · (2024) | TGRS Research Map | TGRS