The objective of a style transfer is to maintain the content of an image while transferring the style of another image. However, conventional research on style transfer has a significant limitation in preserving facial landmarks, such as the eyes, nose, and mouth, which are crucial for maintaining the identity of the image. In Korean portraits, the majority of individuals wear "Gat", a type of headdress exclusively worn by men. Owing to its distinct characteristics from the hair in ID photos, transferring the "Gat" is challenging. To address this issue, this study proposes a deep learning network that can perform style transfer, including the "Gat", while preserving the identity of the face. Unlike existing style transfer approaches, the proposed method aims to preserve texture, costume, and the "Gat" on the style image. The Generative Adversarial Network forms the backbone of the proposed network. The color, texture, and intensity were extracted differently based on the characteristics of each block and layer of the pre-trained VGG-16, and only the necessary elements during training were preserved using a facial landmark mask. The head area was presented using the eyebrow area to transfer the "Gat". Furthermore, the identity of the face was retained, and style correlation was considered based on the Gram matrix. The proposed approach demonstrated superior transfer and preservation performance compared to previous studies.

翻译：风格迁移的目标是在迁移另一幅图像的风格时保留原图像的内容。然而，传统风格迁移研究在保留面部地标（如眼睛、鼻子和嘴巴）方面存在显著局限，而这些地标对于维持图像身份特征至关重要。在韩国肖像画中，多数人物佩戴名为"Gat"的男性专属头饰。由于该头饰与身份证照片中的发型特征迥异，迁移"Gat"极具挑战性。为解决此问题，本研究提出一种深度学习网络，能够在保留面部身份特征的同时实现包含"Gat"在内的风格迁移。与现有风格迁移方法不同，本方法旨在保留风格图像中的纹理、服饰及"Gat"元素。生成对抗网络构成该网络的骨干结构。基于预训练VGG-16各模块与层的特性，分别提取颜色、纹理和强度特征，并利用面部地标掩码仅保留训练过程中的必要元素。通过眉毛区域界定头部范围以实现"Gat"的迁移。此外，面部身份特征得以保留，并基于Gram矩阵考虑风格相关性。与先前研究相比，本方法展现出更优的迁移与保留性能。