Face Synthesis From Near-Infrared To Visual Light Via Sparse Representation

	Face Synthesis From Near-Infrared To Visual Light Via Sparse Representation
	Zeda Zhang; Yunhong Wang; Zhaoxiang Zhang; Guangpeng Zhang
	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
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/13279]
专题	自动化研究所_类脑智能研究中心
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.