Anchored Neighborhood Index for Face Sketch Synthesis

This is the authors' abstract. We don't add key points for this paper.

Exemplar-based face sketch synthesis has long been impeded by the difficulty of accurate neighbor selection. Given a test patch extracted from the test photograph, the K-nearest neighbor (K-NN) matching algorithm is generally performed by existing methods to find K-nearest photograph patches in the training data set, which contains some pairs of face sketches and photographs. Then, the training sketch patches corresponding to the selected nearest photograph patches are taken as the candidate to synthesize the target sketch patch. In the aforementioned neighbor selection process, training sketch patches is not taken into consideration in the process of K-nearest neighbor selection. In this paper, we proposed a simple yet effective neighbor selection algorithm, namely, anchored neighborhood index (ANI), to boost the synthesis performance by taking training sketch patches into the consideration. In addition, the proposed ANI can be conducted offline and, thus, it does not increase the computational complexity. Extensive experiments on public available database demonstrate that the proposed algorithm achieves superior performance compared with the state-of-the-art methods in terms of both objective image quality scores and face recognition accuracy.