Subspace Learning for Face Recognition on Yale database
A random subset with p(=2,3,4,5)
images per individual was taken with labels to form the training
set, and the rest of the database was considered to be the testing
set. For each given p, we average the results over 50 random
splits. Note that, for LDA, there are at most c-1 nonzero
generalized eigenvalues and, so, an upper bound on the dimension of
the reduced space is c-1, where c is the number of individuals.
For the baseline method, the recognition is simply
performed in the original 1024-dimensional image space without any
dimensionality reduction.
LPP vs. LDA vs. PCA
| LPP (PCARatio)
| TensorLPP vs. LPP vs. LDA vs. PCA
| OLPP vs. LPP vs. LDA vs. PCA