Morphing Attack Detection -- Database, Evaluation Platform and Benchmarking
Kiran Raja, Matteo Ferrara, Annalisa Franco, Luuk Spreeuwers, Illias, Batskos, Florens de Wit Marta Gomez-Barrero, Ulrich Scherhag, Daniel Fischer,, Sushma Venkatesh, Jag Mohan Singh, Guoqiang Li, Lo\"ic Bergeron, Sergey, Isadskiy, Raghavendra Ramachandra, Christian Rathgeb

TL;DR
This paper introduces a new diverse dataset and an online platform for benchmarking Morphing Attack Detection algorithms, addressing current limitations in generalizability, diversity, and realistic operational scenarios.
Contribution
It provides a sequestered, diverse facial image dataset and an online evaluation platform to improve benchmarking and generalization of MAD algorithms.
Findings
New dataset with 150 subjects across ethnicities, ages, genders
Images include printed, scanned, and post-processed to simulate real-world conditions
Benchmarking platform enables testing on unseen data for robustness assessment
Abstract
Morphing attacks have posed a severe threat to Face Recognition System (FRS). Despite the number of advancements reported in recent works, we note serious open issues such as independent benchmarking, generalizability challenges and considerations to age, gender, ethnicity that are inadequately addressed. Morphing Attack Detection (MAD) algorithms often are prone to generalization challenges as they are database dependent. The existing databases, mostly of semi-public nature, lack in diversity in terms of ethnicity, various morphing process and post-processing pipelines. Further, they do not reflect a realistic operational scenario for Automated Border Control (ABC) and do not provide a basis to test MAD on unseen data, in order to benchmark the robustness of algorithms. In this work, we present a new sequestered dataset for facilitating the advancements of MAD where the algorithms can…
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