Image translation between human face and wayang orang using U-GAT-IT

Wayang orang performance is one of the Indonesian traditional cultures. The wayang orang players took about an hour to become a proper wayang orang since it takes time to have makeup and to find the appropriate costume before the performance is held. This problem can be solved by developing a computer-based simulation on applying makeup and traditional costume to the face and head of the wayang orang player, respectively. This task can be completed by using image translation. Therefore, people's images can be transformed into wayang orang images. This study aims to translate human faces into wayang orang by adding makeup and accessories using the U-GAT-IT with an unpaired dataset consisting of 1216 data trains and 240 data tests. The challenge of this research is to maintain the image background and the facial identity component in the input image. This research employs quantitative testing employ Kernel Inception Distance (KID), Frèchet Inception Distance (FID), and Inception Score (IS) to evaluate the quality of the output image obtained from the generator. The experimental results show that U-GAT-IT produces a better result than DCLGAN does according to the value of IS, FID, and KID. The IS, FID, and KID obtained by implementing U-GAT-IT are 2.414, 0.924, and 4.357, respectively.