Name
Abstract | ||
|---|---|---|
Face photo-sketch and sketch-photo synthesis have important usages in law enforcement. It is challenging to synthesize face sketches from photos because the drawing techniques and styles of artists' depictions are hard to be learned. To synthesize face photos from sketches is also hard due to its ill-posed nature. In order to avoid mosaic effects in the existed photo-sketch methods, we propose a smoothness-constrained photo-sketch synthesis method via sparse representation. The work is an extension of the previous work[1]. The method is modeled as the minimization of an energy function, a large scale convex optimization problem with l1-norm constraint. Since previous optimization methods are infeasible to solve our problem, we propose an iterative optimization approach, which decomposes the large scale optimization into a sequence of small scale optimizations and solve them iteratively to obtain the approximated optimal solution. The same synthesis strategy can be also used to synthesize photos from sketches. Experiments show its effectiveness. |
Year | Venue | Keywords |
|---|---|---|
2012 | ICPR | image representation,face recognition,l1-norm constraint,image segmentation,mosaic effects,drawing techniques,iterative optimization approach,artist depictions,convex programming,approximated optimal solution,large scale convex optimization problem,law enforcement,face sketch-photo synthesis,energy function minimization,smoothness-constrained face photo-sketch synthesis,ill-posed nature,minimisation,iterative methods,sparse representation |
Field | DocType | ISSN |
Facial recognition system,Computer vision,Computer science,Iterative method,Sparse approximation,Image segmentation,Minimisation (psychology),Minification,Artificial intelligence,Convex optimization,Sketch | Conference | 1051-4651 |
ISBN | Citations | PageRank |
978-1-4673-2216-4 | 0 | 0.34 |
References | Authors | |
4 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liang Chang | 1 | 39 | 5.01 |
| Xiaoming Deng | 2 | 68 | 7.59 |
| Mingquan Zhou | 3 | 266 | 65.04 |
| Fuqing Duan | 4 | 163 | 26.40 |
| Zhongke Wu | 5 | 255 | 52.70 |