#include <opencv2/face/facerec.hpp>

Collaboration diagram for cv::face::EigenFaceRecognizer:

Static Public Member Functions
static Ptr< EigenFaceRecognizer >	create (int num_components=0, double threshold=DBL_MAX)

Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file.

template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String.

template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node.

Additional Inherited Members
Public Member Functions inherited from cv::face::BasicFaceRecognizer
virtual bool	empty () const CV_OVERRIDE

cv::Mat	getEigenValues () const

cv::Mat	getEigenVectors () const

cv::Mat	getLabels () const

cv::Mat	getMean () const

int	getNumComponents () const

std::vector< cv::Mat >	getProjections () const

double	getThreshold () const CV_OVERRIDE

virtual void	read (const FileNode &fn) CV_OVERRIDE

virtual void	read (const FileNode &fn) CV_OVERRIDE=0

virtual void	read (const String &filename)
	Loads a FaceRecognizer and its model state.

void	setNumComponents (int val)

void	setThreshold (double val) CV_OVERRIDE

virtual void	write (const String &filename) const
	Saves a FaceRecognizer and its model state.

virtual void	write (FileStorage &fs) const CV_OVERRIDE

virtual void	write (FileStorage &fs) const CV_OVERRIDE=0

Public Member Functions inherited from cv::face::FaceRecognizer
virtual String	getLabelInfo (int label) const
	Gets string information by label.

virtual std::vector< int >	getLabelsByString (const String &str) const
	Gets vector of labels by string.

int	predict (InputArray src) const

void	predict (InputArray src, int &label, double &confidence) const
	Predicts a label and associated confidence (e.g. distance) for a given input image.

virtual void	predict (InputArray src, Ptr< PredictCollector > collector) const =0
	if implemented - send all result of prediction to collector that can be used for somehow custom result handling

virtual void	setLabelInfo (int label, const String &strInfo)
	Sets string info for the specified model's label.

virtual void	train (InputArrayOfArrays src, InputArray labels)=0
	Trains a FaceRecognizer with given data and associated labels.

virtual void	update (InputArrayOfArrays src, InputArray labels)
	Updates a FaceRecognizer with given data and associated labels.

Public Member Functions inherited from cv::Algorithm
	Algorithm ()

virtual	~Algorithm ()

virtual void	clear ()
	Clears the algorithm state.

virtual String	getDefaultName () const

virtual void	save (const String &filename) const

void	write (const Ptr< FileStorage > &fs, const String &name=String()) const

void	write (FileStorage &fs, const String &name) const

Protected Member Functions inherited from cv::Algorithm
void	writeFormat (FileStorage &fs) const

Protected Attributes inherited from cv::face::BasicFaceRecognizer
Mat	_eigenvalues

Mat	_eigenvectors

Mat	_labels

Mat	_mean

int	_num_components

std::vector< Mat >	_projections

double	_threshold

Protected Attributes inherited from cv::face::FaceRecognizer
std::map< int, String >	_labelsInfo

Member Function Documentation

◆ create()

static Ptr< EigenFaceRecognizer > cv::face::EigenFaceRecognizer::create	(	int	num_components = `0`,
		double	threshold = `DBL_MAX`
	)

static

Python:
	cv.face.EigenFaceRecognizer.create(	[, num_components[, threshold]]	) ->	retval
	cv.face.EigenFaceRecognizer_create(	[, num_components[, threshold]]	) ->	retval

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.

The documentation for this class was generated from the following file:

opencv2/face/facerec.hpp

Static Public Member Functions

Additional Inherited Members

Member Function Documentation

◆ create()

Notes:

Model internal data: