Package 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


Constructor Summary
Constructors Modifier Constructor Description protected
EigenFaceRecognizer(long addr)

Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method 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, predict_collect, predict_label, read, setLabelInfo, train, update, write

Methods inherited from class org.opencv.core.Algorithm
clear, empty, getDefaultName, getNativeObjAddr, save




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. (capslock, 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.
 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. (capslock, 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.
 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. (capslock, 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.
 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 java.lang.Throwable
 Overrides:
finalize
in classBasicFaceRecognizer
 Throws:
java.lang.Throwable

