  Exemplar-based face sketch synthesis plays an important role in both digital
entertainment and law enforcement. It generally consists of two parts: neighbor
selection and reconstruction weight representation. The most time-consuming or
main computation complexity for exemplar-based face sketch synthesis methods
lies in the neighbor selection process. State-of-the-art face sketch synthesis
methods perform neighbor selection online in a data-driven manner by $K$
nearest neighbor ($K$-NN) searching. Actually, the online search increases the
time consuming for synthesis. Moreover, since these methods need to traverse
the whole training dataset for neighbor selection, the computational complexity
increases with the scale of the training database and hence these methods have
limited scalability. In this paper, we proposed a simple but effective offline
random sampling in place of online $K$-NN search to improve the synthesis
efficiency. Extensive experiments on public face sketch databases demonstrate
the superiority of the proposed method in comparison to state-of-the-art
methods, in terms of both synthesis quality and time consumption. The proposed
method could be extended to other heterogeneous face image transformation
problems such as face hallucination. We release the source codes of our
proposed methods and the evaluation metrics for future study online:
http://www.ihitworld.com/RSLCR.html.
