Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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)
Computing 3-D head orientation from a monocular image sequence
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '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
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)
A signal-processing framework for reflection
ACM Transactions on Graphics (TOG)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Dense Stereo with Occlusions for New View-Synthesis by Four-State Dynamic Programming
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time combined 2D+3D active appearance models
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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 robust face recognition using a single image
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
3D face recognition with sparse spherical representations
Pattern Recognition
Automatic face interpretation using fast 3D illumination-based AAM models
Computer Vision and Image Understanding
Robust sparse bounding sphere for 3D face recognition
Image and Vision Computing
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Face recognition under varying pose is a challenging problem, especially when illumination variations are also present. In this paper, we propose to address one of the most challenging scenarios in face recognition. That is, to identify a subject from a test image that is acquired under dierent pose and illumination condition from only one training sample (also known as a gallery image) of this subject in the database. For example, the test image could be semifrontal and illuminated by multiple lighting sources while the corresponding training image is frontal under a single lighting source. Under the assumption of Lambertian reflectance, the spherical harmonics representation has proved to be effective in modeling illumination variations for a fixed pose. In this paper, we extend the spherical harmonics representation to encode pose information. More specifically, we utilize the fact that 2D harmonic basis images at different poses are related by close-form linear transformations, and give a more convenient transformation matrix to be directly used for basis images. An immediate application is that we can easily synthesize a different view of a subject under arbitrary lighting conditions by changing the coefficients of the spherical harmonics representation. A more important result is an efficient face recognition method, based on the orthonormality of the linear transformations, for solving the above-mentioned challenging scenario. Thus, we directly project a nonfrontal view test image onto the space of frontal view harmonic basis images. The impact of some empirical factors due to the projection is embedded in a sparse warping matrix; for most cases, we show that the recognition performance does not deteriorate after warping the test image to the frontal view. Very good recognition results are obtained using this method for both synthetic and challenging real images.