Class FisherFaceRecognizer

• public class FisherFaceRecognizer
extends BasicFaceRecognizer

• Fields inherited from class org.opencv.core.Algorithm

nativeObj
• Constructor Summary

Constructors
Modifier Constructor Description
protected  FisherFaceRecognizer​(long addr)
• Method Summary

All Methods
Modifier and Type Method Description
static FisherFaceRecognizer __fromPtr__​(long addr)
static FisherFaceRecognizer create()
Discriminant Analysis with the Fisherfaces criterion.
static FisherFaceRecognizer create​(int num_components)
static FisherFaceRecognizer create​(int num_components, double threshold)
protected void finalize()
• Methods inherited from class org.opencv.face.BasicFaceRecognizer

getEigenValues, getEigenVectors, getLabels, getMean, getNumComponents, getProjections, getThreshold, setNumComponents, setThreshold
• Methods inherited from class org.opencv.face.FaceRecognizer

getLabelInfo, getLabelsByString, predict, predict_collect, predict_label, read, setLabelInfo, train, update, write
• Methods inherited from class org.opencv.core.Algorithm

clear, empty, getDefaultName, getNativeObjAddr, save
• Methods inherited from class java.lang.Object

clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
• 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