Class FisherFaceRecognizer

java.lang.Object
org.opencv.core.Algorithm
org.opencv.face.FaceRecognizer
org.opencv.face.BasicFaceRecognizer
org.opencv.face.FisherFaceRecognizer
public class FisherFaceRecognizer extends BasicFaceRecognizer

  • Constructor Details

    • FisherFaceRecognizer

      protected FisherFaceRecognizer(long addr)

  • Method Details

    • __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 Throwable
      Overrides:
      finalize in class BasicFaceRecognizer
      Throws:
      Throwable