Towards Better Morphed Face Images without Ghosting Artifacts
Clemens Seibold Anna Hilsmann Peter Eisert
Automatic generation of morphed face images often produces ghosting artifacts
due to poorly aligned structures in the input images. Manual processing can
mitigate these artifacts. However, this is not feasible for the generation of
large datasets, which are required for training and evaluating robust morphing
attack detectors. In this paper, we propose a method for automatic prevention
of ghosting artifacts based on a pixel-wise alignment during morph generation.
We evaluate our proposed method on state-of-the-art detectors and show that our
morphs are harder to detect, particularly, when combined with
style-transfer-based improvement of low-level image characteristics.
Furthermore, we show that our approach does not impair the biometric quality,
which is essential for high quality morphs.