Face Sketch Synthesis From a Single Photo–Sketch Pair
Shengchuan Zhang, Xinbo Gao, Nannan Wang, Jie Li · IEEE Transactions on Circuits and Systems for Video Technology · 2015
Face sketch synthesis is crucial in many practical applications, such as digital entertainment and law enforcement. Previous methods relying on many photo-sketch pairs have made great progress. State-of-the-art face sketch synthesis algorithms adopt Bayesian inference (BI) (e.g., Markov random fields) to select local sketch patches around corresponding position from a set of training data. However, these methods have two limitations: 1) they depend on many training photo-sketch pairs and 2) they cannot tackle nonfacial factors (e.g., hairpins, glasses, backgrounds, and image size) if these factors are excluded in training data. In this paper, we propose a novel face sketch synthesis method that is capable of handling nonfacial factors only using a single photo-sketch pair from coarse to fine. Our method proposes a cascaded image synthesis (CIS) strategy and integrates sparse representation-based greedy search (SRGS) and BI for face sketch synthesis. We first apply SRGS to select candidate sketch patches from the whole training photo-sketch pairs sampled from the only photo-sketch pair. We then employ BI to estimate an initial sketch. Afterward, the input photo and the estimated initial sketch are taken as an additional photo-sketch pair for training. Finally, we adopt CIS with the given two photo-sketch pairs to further improve the quality of the initial sketch. The experimental results on several databases demonstrate that our algorithm outperforms state-of-the-art methods.