  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.
