<p>Face morphing attacks pose a significant threat to modern facial recognition systems by fusing two facial images into a single morphed image. This can deceive biometric systems and lead to inaccurate identifications. Despite various detection methods developed to counter these attacks, restoring the original facial image of the accomplice from the morphed image-known as face de-morphing-remains a substantial challenge. In this paper, we propose a StyleGAN-based face de-morphing network to recover the facial images of the accomplice. Our method utilizes the pre-trained StyleGAN model to encode facial images into the semantic latent space, applies a specially designed lightweight identity feature separation network to obtain high-quality semantic latent encodings of the accomplice, and then employs another pre-trained StyleGAN to generate high-quality restored images. Experimental results demonstrate that our approach significantly improves restoration accuracy compared to existing facial de-morphing methods while maintaining an efficient and lightweight identity separation network.</p>