  A face morph is created by strategically combining two or more face images
corresponding to multiple identities. The intention is for the morphed image to
match with multiple identities. Current morph attack detection strategies can
detect morphs but cannot recover the images or identities used in creating
them. The task of deducing the individual face images from a morphed face image
is known as \textit{de-morphing}. Existing work in de-morphing assume the
availability of a reference image pertaining to one identity in order to
recover the image of the accomplice - i.e., the other identity. In this work,
we propose a novel de-morphing method that can recover images of both
identities simultaneously from a single morphed face image without needing a
reference image or prior information about the morphing process. We propose a
generative adversarial network that achieves single image-based de-morphing
with a surprisingly high degree of visual realism and biometric similarity with
the original face images. We demonstrate the performance of our method on
landmark-based morphs and generative model-based morphs with promising results.
