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for the eigen and fisherfaces model:

  • num_components (to retain from the pca)
  • the mean image
  • the (pca-reduced set of) eigenvectors (multiplied by the lda eigenvec mat for fisherfaces)
  • the eigenvalues (though they are never used in the algorithm)
  • the projected train images
  • the class labels
  • the labels_info (name - id pairs)

for LBPH:

  • radius, neighbours, gridx, gridy (the lbp params, given in the constructor)
  • the precomputed lbp-histograms from the train images
  • the class labels
  • the labels_info (name - id pairs)

it's actually quite, like you expected.

for the eigen and fisherfaces fisherfaces model:

  • num_components (to retain from the pca)
  • the mean image
  • the (pca-reduced set of) eigenvectors (multiplied by the lda eigenvec mat for fisherfaces)
  • the eigenvalues (though they are never used in the algorithm)
  • the projected train images
  • the class labels
  • the labels_info (name - id pairs)

for LBPH:LBPH:

  • radius, neighbours, gridx, gridy (the lbp params, given in the constructor)
  • the precomputed lbp-histograms from the train images
  • the class labels
  • the labels_info (name - id pairs)