## 1 Introduction  
One of the main tasks of the police is to find missing persons or suspected persons ( fugitives). Several methods can be used for this, including fingerprints, dactyloscopy, ballistic methods, lie detection methods, and one of the most widely used, face sketches [1]. Face sketches can provide more comprehensive information regarding a person's physical characteristics. Two of the benefits of face sketches, which Indonesian police have commonly used, are that they empower the public to help the police identify suspect, and they increase alertness in the general population [1]. In addition, face sketches can also be a reference that can be used in searching for people through the all-points bulletin or wanted person database (DPO in Indonesian terminology). Hence, face sketches are still the most effective way to catch suspects. However, making a high-quality face sketch with takes a long time. One example is sketching the faces of the [PERSON] case's suspects, which took up to 109 days after the victim was dosued [2]. Therefore, various studieswere performed with the goal of making the process of searching with face sketches more effective, fast, and accurate. Several research works have proposed methods for translating face sketches into photo images using deep learning. An earlier study by [PERSON] et al. [3] introduced a model developed from a fully convolutional representation learning, which experienced losses in pixel level and in generating texture details. The subsequent work employed generative models; the most widely-used of which is Generative Adversarial Networks (GAN) [4; 5; 6; 7; 8]. [PERSON] et al. [8] developed a GAN-based framework, namely, conditional GAN (cGAN), which is also known as pix2 pix. It deployed U-Net for the encoder-decoder function, PatchGAN for the discriminator, and L\({}_{1}\) term for the loss function. Pix2 pix became a promising framework for extensive image-to-image translation tasks, e.g., sketches to photos, labels to scenes, day to night, etc. However, it failed to capture fine-grained details and photo-realistic textures [7]. Other GAN-based approaches have been extensively proposed. [PERSON] et al. [9] combined a multiscale convolutional neural network (CNN) and a self-attention mechanism to transform face sketches to photos. Despite the fact that the quantitative performance outperformed the state-of-the-art methods, it suffered from a brightness problem affected by the weights of its feature extractor. [PERSON] et al. [7] introduced sketch-to-photo GAN (SPGAN) with a two-stage approach. They applied semantic loss in the first stage, then implemented color refinement loss, texture loss, a multiscale discriminator, and a patch level discriminator in the second stage. It achieved relatively low FID in the datasets. To generate detailed texture while preserving global structural information, [PERSON] et al. [10] proposed a sketch-transformer network consisting of three modules, i.e., a multiscale feature and position encoder, a self-attention module, and a multiscale spatially adaptive denomalization decoder. Compared to SPGAN, it outperformed with a lower FID for the same dataset. However, they have not been trained for Southeast Asian faces, particularly Indonesian faces. The translation of face sketches into photos using GANs has been widely studied and applied by law enforcement agencies abroad [11]. By using realistic photos, the visualizations of fugitives becomes more like real faces, allowing the public to becomes more vigilant. However, GAN-based translation has a high bias towards the dataset used, especially in terms of generating the correct racial face features, including skin tone. Without proper adjustments to the framework, this method is unable to accurately represent Indonesian people. Furthermore, to be usable for Indonesian face sketches, a dataset in the form of pairs of face photos and sketches that can represent Indonesian people is needed, as well as a GAN model consisting of a generator and discriminator.