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

• URL: http://arxiv.org/abs/2411.14494v4
• Date: Fri, 30 May 2025 05:12:38 GMT
• Title: dc-GAN: Dual-Conditioned GAN for Face Demorphing From a Single Morph
• Authors: Nitish Shukla, Arun Ross,
• Abstract summary: Face demorphing attempts to recover the original images constituting a facial morph.<n> dc-GAN (dual-conditioned GAN) is a novel demorphing method conditioned on the morph image as well as the embedding extracted from the image.<n>Our method overcomes the morph replication problem and produces high-fidelity reconstructions of the constituent images.
• Score: 10.902536447343465
• License: http://creativecommons.org/licenses/by/4.0/
• 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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