DiffDeMorph: Extending Reference-Free Demorphing to Unseen Faces

A face morph is created by combining two (or more) face images corresponding to two (or more) identities to produce a composite that successfully matches the constituent identities. Reference-free (RF) demorphing reverses this process using only the morph image, without the need for additional reference images. Previous RF demorphing methods were overly constrained, as they rely on assumptions about the distributions of training and testing morphs such as the morphing technique used, face style, and images used to create the morph. In this paper, we introduce a novel diffusion-based approach, referred to as diffDeMorph, that effectively disentangles component images from a composite morph image with high visual fidelity. Our method is the first to generalize across morph techniques and face styles, beating the current state of the art by ≥ 59.46% under a common training protocol across all datasets tested. We train our method on morphs created using synthetically generated face images and test on real morphs, thereby enhancing the practicality of the technique. Experiments on six datasets and two face matchers establish the utility and efficacy of our method.