Lambertian Reflectance and Linear Subspaces
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)
SFS Based View Synthesis for Robust Face Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
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)
3D face recognition based on high-resolution 3D face modeling from frontal and profile views
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
Constructing Dense Correspondences to Analyze 3D Facial Change
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
3D Assisted Face Recognition: A Survey of 3D Imaging, Modelling and Recognition Approachest
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Journal of Cognitive Neuroscience
Video-rate capture of dynamic face shape and appearance
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Hi-index | 0.00 |
We address the problem of pose and illumination invariance in face recognition and propose to use explicit 3D model and variants of existing algorithms for both pose [Fit01, MSCA04] and illumination normalization [ZS04] prior to applying 2D face recognition algorithm. However, contrary to prior work we will use person specific, rather than general 3D face models. The proposed solution is realistic as for many applications the additional cost of acquiring 3D face images during enrolment of the subjects is acceptable. 3D sensing is not required during normal operation of the face recognition system. The proposed methodology achieves illumination invariance by estimating the illumination sources using the 3D face model. By-product of this process is the recovery of the face skin albedo which can be used as a photometrically normalised face image. Standard face recognition techniques can then be applied to such illumination corrected images.