# Random Sampling for Fast Face Sketch Synthesis

arXiv: https://arxiv.org/abs/1701.01911 · HTML: https://mlsanity.com/paper/1701.01911/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2017.

## Built on by (most cited first, 4 of 4)

- [Facial-Sketch Synthesis: A New Challenge](https://mlsanity.com/paper/2112.15439/) (2021)
- [Unconstrained Face Sketch Synthesis via Perception-Adaptive Network and A New Benchmark](https://mlsanity.com/paper/2112.01019/) (2021)
- [Semi-supervised Cycle-GAN for face photo-sketch translation in the wild](https://mlsanity.com/paper/2307.10281/) (2023)
- [Biphasic Face Photo-Sketch Synthesis via Semantic-Driven Generative Adversarial Network with Graph Representation Learning](https://mlsanity.com/paper/2201.01592/) (2022)

## Abstract (arXiv)

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.