Constructor Detail

• FisherFaceRecognizer

  protected FisherFaceRecognizer​(long addr)

Method Detail

• __fromPtr__

  public static FisherFaceRecognizer __fromPtr__​(long addr)

• create

  public static FisherFaceRecognizer create​(int num_components,
                                            double threshold)

  Parameters:

  num_components - The number of components (read: Fisherfaces) kept for this Linear Discriminant Analysis with the Fisherfaces criterion. It's useful to keep all components, that means the number of your classes c (read: subjects, persons you want to recognize). If you leave this at the default (0) or set it to a value less-equal 0 or greater (c-1), it will be set to the correct number (c-1) automatically.

  threshold - The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1. ### Notes:

  • Training and prediction must be done on grayscale images, use cvtColor to convert between the color spaces.
  • THE FISHERFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL SIZE. (caps-lock, because I got so many mails asking for this). You have to make sure your input data has the correct shape, else a meaningful exception is thrown. Use resize to resize the images.
  • This model does not support updating.

  ### Model internal data:

  • num_components see FisherFaceRecognizer::create.
  • threshold see FisherFaceRecognizer::create.
  • eigenvalues The eigenvalues for this Linear Discriminant Analysis (ordered descending).
  • eigenvectors The eigenvectors for this Linear Discriminant Analysis (ordered by their eigenvalue).
  • mean The sample mean calculated from the training data.
  • projections The projections of the training data.
  • labels The labels corresponding to the projections.

  Returns:

  automatically generated

• create

  public static FisherFaceRecognizer create​(int num_components)

  Parameters:

  num_components - The number of components (read: Fisherfaces) kept for this Linear Discriminant Analysis with the Fisherfaces criterion. It's useful to keep all components, that means the number of your classes c (read: subjects, persons you want to recognize). If you leave this at the default (0) or set it to a value less-equal 0 or greater (c-1), it will be set to the correct number (c-1) automatically. is larger than the threshold, this method returns -1. ### Notes:

  • Training and prediction must be done on grayscale images, use cvtColor to convert between the color spaces.
  • THE FISHERFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL SIZE. (caps-lock, because I got so many mails asking for this). You have to make sure your input data has the correct shape, else a meaningful exception is thrown. Use resize to resize the images.
  • This model does not support updating.

  ### Model internal data:

  • num_components see FisherFaceRecognizer::create.
  • threshold see FisherFaceRecognizer::create.
  • eigenvalues The eigenvalues for this Linear Discriminant Analysis (ordered descending).
  • eigenvectors The eigenvectors for this Linear Discriminant Analysis (ordered by their eigenvalue).
  • mean The sample mean calculated from the training data.
  • projections The projections of the training data.
  • labels The labels corresponding to the projections.

  Returns:

  automatically generated

• create

  public static FisherFaceRecognizer create()

  Discriminant Analysis with the Fisherfaces criterion. It's useful to keep all components, that means the number of your classes c (read: subjects, persons you want to recognize). If you leave this at the default (0) or set it to a value less-equal 0 or greater (c-1), it will be set to the correct number (c-1) automatically. is larger than the threshold, this method returns -1. ### Notes:

  • Training and prediction must be done on grayscale images, use cvtColor to convert between the color spaces.
  • THE FISHERFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL SIZE. (caps-lock, because I got so many mails asking for this). You have to make sure your input data has the correct shape, else a meaningful exception is thrown. Use resize to resize the images.
  • This model does not support updating.

  ### Model internal data:

  • num_components see FisherFaceRecognizer::create.
  • threshold see FisherFaceRecognizer::create.
  • eigenvalues The eigenvalues for this Linear Discriminant Analysis (ordered descending).
  • eigenvectors The eigenvectors for this Linear Discriminant Analysis (ordered by their eigenvalue).
  • mean The sample mean calculated from the training data.
  • projections The projections of the training data.
  • labels The labels corresponding to the projections.

  Returns:

  automatically generated

• finalize

  protected void finalize()
                   throws java.lang.Throwable

  Overrides:

  finalize in class BasicFaceRecognizer

  Throws:

  java.lang.Throwable