Face recognition under varying illumination using gradientfaces
IEEE Transactions on Image Processing
A discriminated correlation classifier for face recognition
Proceedings of the 2010 ACM Symposium on Applied Computing
Recovering facial intrinsic images from a single input
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Eigenlights: recovering illumination from face images
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Face relighting based on multi-spectral quotient image and illumination tensorfaces
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Illumination normalization using self-lighting ratios for 3d2d face recognition
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Complete gradient face: a novel illumination invariant descriptor
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
A study on the effective approach to illumination-invariant face recognition based on a single image
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
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We present a new approach to face relighting by jointly estimating the pose, reflectance functions, and lighting from as few as one image of a face. Upon such estimation, we can synthesize the face image under any prescribed new lighting condition. In contrast to commonly used face shape models or shape-dependent models, we neither recover nor assume the 3-D face shape during the estimation process. Instead, we train a pose- and pixel-dependent subspace model of the reflectance function using a face database that contains samples of pose and illumination for a large number of individuals (e.g., the CMU PIE database and the Yale database). Using this subspace model, we can estimate the pose, the reflectance functions, and the lighting condition of any given face image. Our approach lends itself to practical applications thanks to many desirable properties, including the preservation of the non-Lambertian skin reflectance properties and facial hair, as well as reproduction of various shadows on the face. Extensive experiments show that, compared to recent representative face relighting techniques, our method successfully produces better results, in terms of subjective and objective quality, without reconstructing a 3-D shape.