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Face Synthesis From Near-Infrared To Visual Light Via Sparse Representation
Zeda Zhang; Yunhong Wang; Zhaoxiang Zhang; Guangpeng Zhang
Conference NameInternational Joint Conference on Biometrics
Source PublicationIJCB 2011
Conference Date11-13 October 2011
Conference PlaceWashington, DC, USA
AbstractThis 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.
KeywordTraining Principal Component Analysis Encoding Educational Institutions Accuracy
Document Type会议论文
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
GB/T 7714
Zeda Zhang,Yunhong Wang,Zhaoxiang Zhang,et al. Face Synthesis From Near-Infrared To Visual Light Via Sparse Representation[C],2011.
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