Robust Face Sketch Synthesis via Generative Adversarial Fusion of Priors and Parametric Sigmoid

Robust Face Sketch Synthesis via Generative Adversarial Fusion of Priors and Parametric Sigmoid

Shengchuan Zhang, Rongrong Ji, Jie Hu, Yue Gao, Chia-Wen Lin

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 1163-1169. https://doi.org/10.24963/ijcai.2018/162

Despite the extensive progress in face sketch synthesis, existing methods are mostly workable under constrained conditions, such as fixed illumination, pose, background and ethnic origin that are hardly to control in real-world scenarios. The key issue lies in the difficulty to use data under fixed conditions to train a model against imaging variations. In this paper, we propose a novel generative adversarial network termed pGAN, which can generate face sketches efficiently using training data under fixed conditions and handle the aforementioned uncontrolled conditions. In pGAN, we embed key photo priors into the process of synthesis and design a parametric sigmoid activation function for compensating illumination variations. Compared to the existing methods, we quantitatively demonstrate that the proposed method can work well on face photos in the wild.
Keywords:
Computer Vision: Biometrics, Face and Gesture Recognition
Computer Vision: Computer Vision