Recently, automatic face sketch synthesis gets more and more attention due to its applications in law enforcement and digital entertainment. For example, in law enforcement, it is useful to develop a system for searching photos from police mug-shot databases by using a sketch drawing where a photo of a suspect is not available. By transferring face photos to sketches, a sketch drawn by a painter may be directly matched with a sketch automatically synthesized by a computer, so that the authenticity of face recognition may be improved. In the movie industry, artists may save a great amount of time on drawing cartoon faces with an assistance of an automatic sketch synthesis system. Such a system may also make people to easily personalize their identities in the digital world, such as a MSN avatar.
Popular sketch synthesis methods are mostly based on a sample library, which generates a sketch with rich textures from an input face photo based on a set of training face photo-sketch pairs. These approaches can synthesize sketches of different styles by choosing different style of training sets. In the prior art, it is proposed to apply a global feature transform to synthesize a sketch from a photo. However, such a global linear model will not work well if the hair region is included, since the hair styles vary significantly from different people. To overcome this limitation, a patch-based reconstruction is proposed. The drawback of this approach is that the patches in each area are synthesized independently such that their spatial relationships are ignored. Therefore, some face structures cannot be well synthesized. In addition, face sketch synthesis through linear combinations of training sketch patches causes the blurring effect.
In a word, previous methods only work under well controlled conditions and often fail when there are variations of lighting and pose.