Face sketch-to-photo synthesis from simple line drawing

Face sketch-to-photo synthesis has attracted increasing attention in recent years for its useful applications on both digital entertainment and law enforcement. Although great progress has been made, previous methods only work on face sketches with rich textures which are not easily to obtain. In this paper, we propose a robust algorithm for synthesizing a face photo from a simple line drawing that contains only a few lines without any texture. In order to obtain a robust result, firstly, the input sketch is divided into several patches and edge descriptors are extracted from these local input patches. Afterwards, an MRF framework is built based on the divided local patches. Then a series of candidate photo patches are synthesized for each local sketch patch based on a coupled dictionary learned from a set of training data. Finally, the MRF is optimized to get the final estimated photo patches for each input sketch patch and a realistic face photo is synthesized. Experimental results on CUHK database have validated the effectiveness of the proposed method.

[1]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[2]  Chun Chen,et al.  Image-based facial sketch-to-photo synthesis via online coupled dictionary learning , 2012, Inf. Sci..

[3]  Wei Liu,et al.  Bayesian Tensor Inference for Sketch-Based Facial Photo Hallucination , 2007, IJCAI.

[4]  Xiaogang Wang,et al.  Face sketch recognition , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[5]  Amit R.Sharma,et al.  Face Photo-Sketch Synthesis and Recognition , 2012 .

[6]  Yihong Gong,et al.  Locality-constrained Linear Coding for image classification , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Yihong Gong,et al.  Nonlinear Learning using Local Coordinate Coding , 2009, NIPS.

[8]  Chun-xia Yang,et al.  A method of illumination compensation for human face image based on quotient image , 2008, Inf. Sci..

[9]  Weilin Huang,et al.  Adaptive nonlinear manifolds and their applications to pattern recognition , 2010, Inf. Sci..

[10]  Stephen Mancusi The Police Composite Sketch , 2010 .

[11]  Yong Jae Lee,et al.  ShadowDraw: real-time user guidance for freehand drawing , 2011, ACM Trans. Graph..

[12]  Hanqing Lu,et al.  A nonlinear approach for face sketch synthesis and recognition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  Xuelong Li,et al.  Geometric Mean for Subspace Selection , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  William T. Freeman,et al.  Learning Low-Level Vision , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[15]  C. Lawrence Zitnick,et al.  Binary Coherent Edge Descriptors , 2010, ECCV.

[16]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Xuelong Li,et al.  A new approach for face recognition by sketches in photos , 2009, Signal Process..

[18]  Matthew A. Brown,et al.  Learning Local Image Descriptors , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Matthew A. Brown,et al.  Picking the best DAISY , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  William T. Freeman,et al.  Understanding belief propagation and its generalizations , 2003 .