Neural Probabilistic Graphical Model for Face Sketch Synthesis

Neural network learning for face sketch synthesis from photos has attracted substantial attention due to its favorable synthesis performance. However, most existing deep-learning-based face sketch synthesis models stacked only by multiple convolutional layers without structured regression often lose the common facial structures, limiting their flexibility in a wide range of practical applications, including intelligent security and digital entertainment. In this article, we introduce a neural network to a probabilistic graphical model and propose a novel face sketch synthesis framework based on the neural probabilistic graphical model (NPGM) composed of a specific structure and a common structure. In the specific structure, we investigate a neural network for mapping the direct relationship between training photos and sketches, yielding the specific information and characteristic features of a test photo. In the common structure, the fidelity between the sketch pixels generated by the specific structure and their candidates selected from the training data are considered, ensuring the preservation of the common facial structure. Experimental results on the Chinese University of Hong Kong face sketch database demonstrate, both qualitatively and quantitatively, that the proposed NPGM-based face sketch synthesis approach can more effectively capture specific features and recover common structures compared with the state-of-the-art methods. Extensive experiments in practical applications further illustrate that the proposed method achieves superior performance.