A face morph is created by combining the face images usually pertaining to two distinct identities. The goal is to generate an image that can be matched with two identities thereby undermining the security of a face recognition system. To deal with this problem, several morph attack detection techniques have been developed. But these methods do not extract any information about the underlying bonafides used to create them. Demorphing addresses this limitation. However, current demorphing techniques are mostly reference-based, i.e, they need an image of one of the identities to recover the other. In this work, we treat demorphing as an ill-posed decomposition problem. We propose a novel method that is reference-free and recovers the bonafides with high accuracy. Our method decomposes the morph into several identity-preserving feature components. A merger network then weighs and combines these components to recover the bonafides. Our method is observed to reconstruct high-quality bonafides in terms of definition and fidelity. Experiments on the CASIA-WebFace, SMDD and AMSL datasets demonstrate the effectiveness of our method.

翻译：人脸变形图像通常通过融合两个不同身份的人脸图像生成，其目标是创建一幅能与两个身份同时匹配的图像，从而破坏人脸识别系统的安全性。为应对此问题，已开发出多种变形攻击检测技术。但这些方法无法提取用于创建变形图像的真实身份信息。去变形技术旨在解决这一局限。然而，当前的去变形方法大多基于参考图像，即需要其中一个身份的图像才能恢复另一个身份。本研究将去变形视为一个不适定的分解问题，提出了一种无需参考图像且能高精度恢复真实身份的新方法。该方法将变形图像分解为多个保持身份特征的分量，随后通过融合网络对这些分量进行加权组合以恢复真实身份。实验表明，该方法在清晰度与保真度方面均能重建高质量的真实身份图像。在CASIA-WebFace、SMDD和AMSL数据集上的实验验证了本方法的有效性。