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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Liang Chang Xiaoming Deng Mingquan Zhou Fuqing Duan Zhongke Wu |
| Copyright Year | 2012 |
| Description | Author affiliation: College of Information Science and Technology, Beijing Normal University, China (Liang Chang; Mingquan Zhou; Fuqing Duan; Zhongke Wu) || Institute of Software, Chinese Academy of Sciences, China (Xiaoming Deng) |
| 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 l-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. |
| Starting Page | 3025 |
| Ending Page | 3029 |
| File Size | 1603359 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781467322164 |
| ISSN | 10514651 |
| e-ISBN | 9784990644109 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-11-11 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | ICPR Org Committee |
| Subject Keyword | Face Dictionaries Optimization Vectors Encoding Training Face recognition |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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