Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

@inproceedings{Belhumeur1996EigenfacesVF,
  title={Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection},
  author={P. Belhumeur and J. Hespanha and D. Kriegman},
  booktitle={ECCV},
  year={1996}
}
We develop a face recognition algorithm which is insensitive to gross variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face under varying illumination direction lie in a 3-D linear subspace of the high dimensional feature space — if the face is a Lambertian surface without self-shadowing. However… Expand
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
Face detection in gray scale images using locally linear embeddings
Subspace Linear Discriminant Analysis for Face RecognitionW
Regularized D-LDA for face recognition
Learning a locality preserving subspace for visual recognition
Face Recognition Using L-Fisherfaces
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 30 REFERENCES
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
Face recognition using eigenfaces
  • M. Turk, A. Pentland
  • Computer Science
  • Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
  • 1991
Eigenfaces for Recognition
Human face recognition method based on the statistical model of small sample size
Face Recognition: the Problem of Compensating for Changes in Illumination Direction
View-based and modular eigenspaces for face recognition
Face recognition under varying pose
  • D. Beymer
  • Computer Science
  • 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
  • 1994
Dimensionality of illumination in appearance matching
  • S. Nayar, H. Murase
  • Mathematics, Computer Science
  • Proceedings of IEEE International Conference on Robotics and Automation
  • 1996
A unified approach to coding and interpreting face images
Automatic recognition of human facial expressions
...
1
2
3
...