Height and gradient from shading
International Journal of Computer Vision
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
What is the set of images of an object under all possible lighting conditions?
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Clustering Appearances of 3D Objects
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Illumination Cones for Recognition under Variable Lighting: Faces
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Active Appearance Models Revisited
International Journal of Computer Vision
Simultaneous Optimization of Class Configuration and Feature Space for Object Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Object recognition based on photometric alignment using ransac
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Image-based BRDF measurement including human skin
EGWR'99 Proceedings of the 10th Eurographics conference on Rendering
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In this paper, we propose a new method for face recognition under varying illumination conditions using a single input image. Our method is based on a statistical shape-from-shading method which combines the strengths of the Lambertian model and statistical information obtained from a large number of images of different people under varying illumination. The main advantage of our method over the previous methods is that our method explicitly incorporates a correlation between surface points on a face in the MAP estimation of surface normals and albedos, so that a new image of the same face under novel illumination can be synthesized correctly even when the face is partially shadowed. Furthermore, our method introduces pixel grouping and reliability measure in the MAP estimation in order to reduce computational cost while maintaining accuracy. We demonstrate the effectiveness of our proposed method via experiments with real images.