Face Synthesis From Near-Infrared To Visual Light Via Sparse Representation
文献类型：会议论文
| 作者 | Zeda Zhang; Yunhong Wang; Zhaoxiang Zhang ; Guangpeng Zhang
|
| 出版日期 | 2011-10-11 |
| 会议日期 | 11-13 October 2011 |
| 会议地点 | Washington, DC, USA |
| 关键词 | Training Principal Component Analysis Encoding Educational Institutions Accuracy |
| 英文摘要 | This paper presents a novel method for synthesizing artificial visual light (VIS) face images from near-infrared (NIR) inputs. Active NIR imaging is now widely employed because it is unobtrusive, invariant of environmental illuminations, and can penetrate glasses and sweats. Unfortunately, NIR imaging exhibits discrepant photic properties compared with VIS imaging. Based on recent results of re search on compressive sensing, natural images can be compressed and recovered with an overcomplete dictionary by sparse representation coefficients. In our approach a pair wise dictionary is trained from randomly sampled coupled face patches, which contains sparse coded base functions to reconstruct representation coefficients via l1-minimization. We will demonstrate that this method is robust to moderate pose and expression variations, and is efficient in computing. Comparative experiments are conducted with state-of the-art algol1-minimization. We will demonstraterithms. |
| 会议录 | IJCB 2011
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/13279]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Zhaoxiang Zhang |
| 推荐引用方式 GB/T 7714 | Zeda Zhang,Yunhong Wang,Zhaoxiang Zhang,et al. Face Synthesis From Near-Infrared To Visual Light Via Sparse Representation[C]. 见:. Washington, DC, USA. 11-13 October 2011. |
入库方式： OAI收割
来源：自动化研究所
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