Class EigenFaceRecognizer


public class EigenFaceRecognizer extends BasicFaceRecognizer
  • Constructor Details

    • EigenFaceRecognizer

      protected EigenFaceRecognizer(long addr)
  • Method Details

    • __fromPtr__

      public static EigenFaceRecognizer __fromPtr__(long addr)
    • create

      public static EigenFaceRecognizer create(int num_components, double threshold)
      Parameters:
      num_components - The number of components (read: Eigenfaces) kept for this Principal Component Analysis. As a hint: There's no rule how many components (read: Eigenfaces) should be kept for good reconstruction capabilities. It is based on your input data, so experiment with the number. Keeping 80 components should almost always be sufficient.
      threshold - The threshold applied in the prediction. ### Notes:
      • Training and prediction must be done on grayscale images, use cvtColor to convert between the color spaces.
      • THE EIGENFACES 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 EigenFaceRecognizer::create.
      • threshold see EigenFaceRecognizer::create.
      • eigenvalues The eigenvalues for this Principal Component Analysis (ordered descending).
      • eigenvectors The eigenvectors for this Principal Component Analysis (ordered by their eigenvalue).
      • mean The sample mean calculated from the training data.
      • projections The projections of the training data.
      • labels The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.
      Returns:
      automatically generated
    • create

      public static EigenFaceRecognizer create(int num_components)
      Parameters:
      num_components - The number of components (read: Eigenfaces) kept for this Principal Component Analysis. As a hint: There's no rule how many components (read: Eigenfaces) should be kept for good reconstruction capabilities. It is based on your input data, so experiment with the number. Keeping 80 components should almost always be sufficient. ### Notes:
      • Training and prediction must be done on grayscale images, use cvtColor to convert between the color spaces.
      • THE EIGENFACES 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 EigenFaceRecognizer::create.
      • threshold see EigenFaceRecognizer::create.
      • eigenvalues The eigenvalues for this Principal Component Analysis (ordered descending).
      • eigenvectors The eigenvectors for this Principal Component Analysis (ordered by their eigenvalue).
      • mean The sample mean calculated from the training data.
      • projections The projections of the training data.
      • labels The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.
      Returns:
      automatically generated
    • create

      public static EigenFaceRecognizer create()
      Component Analysis. As a hint: There's no rule how many components (read: Eigenfaces) should be kept for good reconstruction capabilities. It is based on your input data, so experiment with the number. Keeping 80 components should almost always be sufficient. ### Notes:
      • Training and prediction must be done on grayscale images, use cvtColor to convert between the color spaces.
      • THE EIGENFACES 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 EigenFaceRecognizer::create.
      • threshold see EigenFaceRecognizer::create.
      • eigenvalues The eigenvalues for this Principal Component Analysis (ordered descending).
      • eigenvectors The eigenvectors for this Principal Component Analysis (ordered by their eigenvalue).
      • mean The sample mean calculated from the training data.
      • projections The projections of the training data.
      • labels The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.
      Returns:
      automatically generated
    • finalize

      protected void finalize() throws Throwable
      Overrides:
      finalize in class BasicFaceRecognizer
      Throws:
      Throwable