Coupled Spectral Regression for Matching Heterogeneous Faces

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Coupled Spectral Regression for Matching Heterogeneous Faces
Face recognition algorithms need to deal with variable lighting conditions. Near infrared (NIR) image based face recognition technology has been proposed to effectively overcome this difficulty. However, it requires that the enrolled face images be captured using NIR images whereas many applications require visual (VIS) images for enrollment templates. To take advantage of NIR face images for illumination-invariant face recognition and allow the use of VIS face images for enrollment, we encounter a new face image pattern recognition problem, that is, heterogeneous face matching between NIR versus VIS faces. In this paper, we present a subspace learning framework named Coupled Spectral Regression (CSR) to solve this challenge problem of coupling the two types of face images and matching between them. CSR first models the properties of different types of data separately and then learns two associated projections to project heterogeneous data (e.g. VIS and NIR) respective...
Stan Z. Li, Zhen Lei
Added 09 May 2009
Updated 10 Dec 2009
Type Conference
Year 2009
Where CVPR
Authors Stan Z. Li, Zhen Lei
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