org.opencv.face

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

Field Summary

Fields inherited from class org.opencv.core.Algorithm

nativeObj

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	FisherFaceRecognizer(long addr)

Method Summary

Modifier and Type	Method and 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_collect, predict_label, predict, 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 Throwable

Overrides:

finalize in class BasicFaceRecognizer

Throws:

Throwable