Deep Latent Low-Rank Representation for Face Sketch Synthesis

Face sketch synthesis is useful and profitable in digital entertainment. Most existing face sketch synthesis methods rely on the assumption that facial photographs/sketches form a low-dimensional manifold. Once the training data are insufficient, the manifold could not characterize the identity-specific information that is included in a test photograph but excluded in the training data. Thus, the synthesized sketch would lose this information, such as glasses, earrings, hairstyles, and hairpins. To provide the sufficient data and satisfy the assumption on manifold, we propose a novel face sketch synthesis framework based on deep latent low-rank representation (DLLRR) in this paper. The DLLRR induces the hidden training sketches with the identity-specific information as the hidden data to the insufficient original training sketches as the observed data. And it searches the lowest rank representation on the candidates of a test photograph from the both hidden and observed data. For the strong representational capability of the coupled autoencoder, we leverage it to reveal the hidden data. Experiment results on face photograph-sketch database illustrate that the proposed method can successfully provide the sufficient training data with the identity-specific information. And compared to the state of the arts, the proposed method synthesizes more clean and vivid face sketches.