org.opencv.face

Class EigenFaceRecognizer

java.lang.Object

org.opencv.core.Algorithm

org.opencv.face.FaceRecognizer

org.opencv.face.BasicFaceRecognizer

org.opencv.face.EigenFaceRecognizer

public class EigenFaceRecognizer
extends BasicFaceRecognizer

Field Summary

Fields inherited from class org.opencv.core.Algorithm

nativeObj

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	EigenFaceRecognizer(long addr)

Method Summary

Modifier and Type	Method and Description
static EigenFaceRecognizer	__fromPtr__(long addr)
static EigenFaceRecognizer	create() Component Analysis.
static EigenFaceRecognizer	create(int num_components)
static EigenFaceRecognizer	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

EigenFaceRecognizer

protected EigenFaceRecognizer(long addr)

Method Detail

__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