dc-GAN - Dual Conditioned GAN for Face Demorphing From a Single Morph

Abstract

A facial morph is an image strategically created by combining two face images pertaining to two distinct identities. The goal is to create a face image that can be matched to two different identities by a face matcher. Face demorphing inverts this process and attempts to recover the original images constituting a facial morph. Existing demorphing techniques have two major limitations: (a) they assume that some identities are common in the train and test sets; and (b) they are prone to the morph replication problem, where the outputs are merely replicates of the input morph. In this paper, we overcome these issues by proposing dc-GAN (dual-conditioned GAN), a novel demorphing method conditioned on the morph image as well as the embedding extracted from the image. Our method overcomes the morph replication problem and produces high-fidelity reconstructions of the constituent images. Moreover, the proposed method is highly generalizable and applicable to both reference-based and reference-free demorphing methods. Experiments were conducted using the AMSL, FRLL-Morphs, and MorDiff datasets to demonstrate the efficacy of the method.

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Citation

If you use this work, please cite:

@INPROCEEDINGS{11099072,
  author={Shukla, Nitish and Ross, Arun},
  booktitle={2025 IEEE 19th International Conference on Automatic Face and Gesture Recognition (FG)}, 
  title={dc-GAN: Dual-Conditioned GAN for Face Demorphing From a Single Morph}, 
  year={2025},
  volume={},
  number={},
  pages={1-9},
  keywords={Face recognition;Gesture recognition;Image reconstruction},
  doi={10.1109/FG61629.2025.11099072}}