  State-of-the-art face recognition (FR) approaches have shown remarkable
results in predicting whether two faces belong to the same identity, yielding
accuracies between 92% and 100% depending on the difficulty of the protocol.
However, the accuracy drops substantially when exposed to morphed faces,
specifically generated to look similar to two identities. To generate morphed
faces, we integrate a simple pretrained FR model into a generative adversarial
network (GAN) and modify several loss functions for face morphing. In contrast
to previous works, our approach and analyses are not limited to pairs of
frontal faces with the same ethnicity and gender. Our qualitative and
quantitative results affirm that our approach achieves a seamless change
between two faces even in unconstrained scenarios. Despite using features from
a simpler FR model for face morphing, we demonstrate that even recent FR
systems struggle to distinguish the morphed face from both identities obtaining
an accuracy of only 55-70%. Besides, we provide further insights into how
knowing the FR system makes it particularly vulnerable to face morphing
attacks.
