Shiqerukaj, ElidonaRathgeb, ChristianMerkle, JohannesDrozdowski, PawelTams, BenjaminBrömme, ArslanDamer, NaserGomez-Barrero, MartaRaja, KiranRathgeb, ChristianSequeira Ana F.Todisco, MassimilianoUhl, Andreas2022-10-272022-10-272022978-3-88579-723-4https://dl.gi.de/handle/20.500.12116/39690Algorithm fusion is frequently employed to improve the accuracy of pattern recognition tasks. This particularly applies to biometrics including attack detection mechanisms. In this work, we apply a fusion of two differential morphing attack detection methods, i.e. Demorphing and Deep Face Representations. Experiments are performed in a cross-database scenario using high-quality face morphs along with realistic live captures. Obtained results reveal that a weighted sum-based score-level fusion of Demorphing and Deep Face Representations improves the morphing attack detection accuracy. With the proposed fusion, a detection equal error rate of 4.9% is achieved, compared to detection equal error rates of 5.6% and 5.8% of the best individual morphing attack detection methods, respectively.enFace recognitionmorphing attack detectionfusiondemorphingdeep face representationsText/Conference Paper10.1109/BIOSIG55365.2022.98970231617-5481