用双条件生成对抗网络实现单张人脸合成图的高保真还原。
dc-GAN: Dual-Conditioned GAN for Face Demorphing From a Single Morph
- 以输入图像和提取嵌入为双重条件，防止输出复制输入
- 在AMSL等三个数据集上重建质量显著优于现有方法
- 适用于有参考和无参考两种场景，通用性强
人脸合成图是通过融合两张不同身份的人脸图像生成的，目标是使该图像能被人脸识别系统匹配到两个不同身份。人脸反合成旨在逆向还原构成合成图的原始人脸图像。现有方法存在两大局限：(a) 假设训练与测试集中存在共同身份；(b) 易出现合成图复制问题，即输出仅为输入的重复。本文提出dc-GAN（双条件生成对抗网络），以合成图及图像嵌入作为双重条件，克服了复制问题，实现了高质量原始图像重建。该方法具备强泛化能力，适用于参考型与无参考型反合成任务。实验在AMSL、FRLL-Morphs和MorDiff数据集上验证了其有效性。
原文摘要 · Abstract (English)
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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