Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symmetric Shape-from-Shading Using Self-ratio Image
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lambertian Reflectance and Linear Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning bilinear models for two-factor problems in vision.
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
SFS Based View Synthesis for Robust Face Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
MVIEW '99 Proceedings of the IEEE Workshop on Multi-View Modeling & Analysis of Visual Scenes
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 Under Varying Pose
Face Recognition Under Varying Pose
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)
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Face recognition under variable lighting using harmonic image exemplars
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Pose-encoded spherical harmonics for face recognition and synthesis using a single image
EURASIP Journal on Advances in Signal Processing
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Face recognition under varying pose is a challenging problem, especially when illumination variations are also present. Under Lambertian model, spherical harmonics representation has proved to be effective in modelling illumination variations for a given pose. In this paper, we extend the spherical harmonics representation to encode pose information. More specifically, we show that 2D harmonic basis images at different poses are related by close-form linear combinations. This enables an analytic method for generating new basis images at a different pose which are typically required to handle illumination variations at that particular pose. Furthermore, the orthonormality of the linear combinations is utilized to propose an efficient method for robust face recognition where only one set of front-view basis images per subject is stored. In the method, we directly project a rotated testing image onto the space of front-view basis images after establishing the image correspondence. Very good recognition results have been demonstrated using this method.