  A morphed face image is a synthetically created image that looks so similar
to the faces of two subjects that both can use it for verification against a
biometric verification system. It can be easily created by aligning and
blending face images of the two subjects. In this paper, we propose a style
transfer based method that improves the quality of morphed face images. It
counters the image degeneration during the creation of morphed face images
caused by blending. We analyze different state of the art face morphing attack
detection systems regarding their performance against our improved morphed face
images and other methods that improve the image quality. All detection systems
perform significantly worse, when first confronted with our improved morphed
face images. Most of them can be enhanced by adding our quality improved morphs
to the training data, which further improves the robustness against other means
of quality improvement.
