Abstract
The purpose of face sketch synthesis technique is to generate a sketch from an input face image given a set of face sketch-photo images as the training set. Since face sketch synthesis is attracting huge attentions, an experimental study to existing methods is nontrivial. This paper provides a comprehensive review and comparative study to representative face sketch synthesis methods. These methods are further sub-divided into two main categories: data-driven methods, also known as exemplar-based methods, and model-driven methods. Generally, exemplar-based face sketch synthesis consists of four parts: patch representation; neighbor selection; weight computation; and patch assembling. Model-driven methods explicitly learn the mapping from face photos to face sketches. We have drawn some promising conclusions in this paper which have not been investigated before.
Highlights
A Comparative Study on Face Sketch SynthesisADEEL AKRAM1, NANNAN WANG2, (Member, IEEE), JIE LI1, AND XINBO GAO 1, (Senior Member, IEEE)
Face sketch synthesis means to generate sketches of input face photos from the training dataset that contains sketchphoto image pairs
The CUFS database contains face images from three sub-datasets: the Chinese University of Hong Kong (CUHK) student database [12] consists of 188 face sketch and photo pairs in which 88 pairs are used for training dataset and the rest 100 pairs are for test dataset, the Aleix Robert (AR) database [24] includes 123 face sketch-photo images pairs in which 80 pairs are taken as the training set and the rest 43 are used for testing setïijŇ and the extended multi modal verification for teleservices and security application XM2VTS database [25] consists of 295 face sketch and photo pairs in which 100 pairs are chosen as the training dataset randomly and the rest 195 are for testing dataset
Summary
ADEEL AKRAM1, NANNAN WANG2, (Member, IEEE), JIE LI1, AND XINBO GAO 1, (Senior Member, IEEE).
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