Face de-morphing based on identity feature transfer

Abstract

Face morphing attacks have emerged as a significant security threat, compromising the reliability of facial recognition systems. Despite extensive research on morphing detection, limited attention has been given to restoring accomplice face images, which is critical for forensic applications. This study aims to address this gap by proposing a novel face de-morphing (FD) method based on identity feature transfer for restoring accomplice face images. The method encodes facial attribute and identity features separately and employs cross-attention mechanisms to extract identity features from morphed faces relative to reference images. This process isolates and enhances the accomplice's identity features. Additionally, inverse linear interpolation is applied to transfer identity features to attribute features, further refining the restoration process. The enhanced identity features are then integrated with the StyleGAN generator to reconstruct high-quality accomplice facial images. Experimental evaluations on two morphed face datasets demonstrate the effectiveness of the proposed approach, improving the average restoration accuracy by at least 5% compared with other methods. These findings highlight the potential of this approach for advancing forensic and security applications.