FisherFaceRecognizer

Objective-C

@interface FisherFaceRecognizer : BasicFaceRecognizer

Swift

class FisherFaceRecognizer : BasicFaceRecognizer

The FisherFaceRecognizer module

Member of Face

Methods

  • Declaration

    Objective-C

    + (nonnull FisherFaceRecognizer *)create:(int)num_components
                               threshold:(double)threshold;

    Swift

    class func create(num_components: Int32, threshold: Double) -> FisherFaceRecognizer

    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.

  • Declaration

    Objective-C

    + (nonnull FisherFaceRecognizer *)create:(int)num_components;

    Swift

    class func create(num_components: Int32) -> FisherFaceRecognizer

    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.
    		 
  •  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.

    Declaration

    Objective-C

    + (nonnull FisherFaceRecognizer *)create;

    Swift

    class func create() -> FisherFaceRecognizer