Class EigenFaceRecognizer

• public class EigenFaceRecognizer
extends BasicFaceRecognizer

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

nativeObj
• Constructor Summary

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

All 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
• 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 java.lang.Throwable
Overrides:
finalize in class BasicFaceRecognizer
Throws:
java.lang.Throwable