Second Competition on Presentation Attack Detection on ID Card

TL;DR

This paper reports on the second ID card presentation attack detection competition, highlighting new evaluation tools, datasets, and improved results, indicating progress but ongoing challenges in PAD accuracy.

Contribution

It introduces an automatic benchmarking platform, new datasets, and two evaluation tracks, advancing the state of ID card PAD research and providing a comprehensive comparison of algorithms.

Findings

Significant improvement in EER and AV-Rank over the first edition.

The best team achieved an EER of 6.36%, showing progress.

ID card PAD remains challenging due to limited image datasets.

Abstract

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 AV-Rank 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 AV-Rank 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.