Adaptive histogram equalization and its variations
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Face Sketch Synthesis and Recognition
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Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Nonlinear Approach for Face Sketch Synthesis and Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Face Photo-Sketch Synthesis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Circuits and Systems for Video Technology
Heterogeneous image transformation
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International Journal of Computer Vision
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Automatic face sketch synthesis has important applications in law enforcement and digital entertainment. Although great progress has been made in recent years, previous methods only work under well controlled conditions and often fail when there are variations of lighting and pose. In this paper, we propose a robust algorithm for synthesizing a face sketch from a face photo taken under a different lighting condition and in a different pose than the training set. It synthesizes local sketch patches using a multiscale Markov Random Field (MRF) model. The robustness to lighting and pose variations is achieved in three steps. Firstly, shape priors specific to facial components are introduced to reduce artifacts and distortions caused by variations of lighting and pose. Secondly, new patch descriptors and metrics which are more robust to lighting variations are used to find candidates of sketch patches given a photo patch. Lastly, a smoothing term measuring both intensity compatibility and gradient compatibility is used to match neighboring sketch patches on the MRF network more effectively. The proposed approach significantly improves the performance of the state-of-the-art method. Its effectiveness is shown through experiments on the CUHK face sketch database and celebrity photos collected from the web.