This paper considers the problem of face sketch synthesis in the wild, which transforms a face photo into a face sketch. Face sketch synthesis is widely applied in law enforcement as well as digital entertainment fields. However, the existing methods either focus on hand-crafted techniques where prior human experience is relied on or adopt deep learning techniques as an end-to-end framework, where facial details cannot be well represented. In this paper, we propose a novel approach for face sketch synthesis in the wild via a deep patch representation-based probabilistic graphical model (DeepPGM). A Siamese network is constructed to extract deep patch representation from a raw facial patch, where the representative detail information for robust face sketch synthesis can be exploited. The generated deep patch representation and facial image patches are then optimally combined through a probabilistic graphical model. The proposed DeepPGM approach not only outperforms the state-of-the-art on public face sketch datasets but also can cope with forensic photos in the wild conditions, including varying lightings, poses, occlusions, skin colors, and ethnic origins. The superiority of the proposed method is demonstrated by extensive experiments on two public face sketch datasets and real-world forensic photos in the wild.

中文翻译：

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基于深度补丁表示的概率图形模型在野外进行人脸草图合成

本文考虑了野外人脸草图合成的问题，该问题将人脸照片转换为人脸草图。人脸素描合成已广泛应用于执法以及数字娱乐领域。但是，现有方法要么专注于依靠以前的人类经验的手工技术，要么采用深度学习技术作为无法很好地表示面部细节的端到端框架。在本文中，我们提出了一种通过基于深度补丁表示的概率图形模型（DeepPGM）在野外进行人脸草图合成的新方法。构建了一个暹罗网络以从原始面部补丁中提取深层补丁表示，可以利用其中的代表性细节信息进行​​鲁棒的面部草图合成。然后，通过概率图形模型将生成的深度补丁表示和面部图像补丁进行最佳组合。提议的DeepPGM方法不仅优于公开的面部素描数据集上的最新技术，而且还可以应对野外条件下的法医照片，包括变化的照明，姿势，遮挡，肤色和种族。通过在两个公开的面部素描数据集和野外真实世界的取证照片上进行的大量实验，证明了该方法的优越性。