cv::face::EigenFaceRecognizer Class Reference

#include <opencv2/face/facerec.hpp>

Inheritance diagram for cv::face::EigenFaceRecognizer:

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

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
void	setNumComponents (int val)
void	setThreshold (double val) CV_OVERRIDE
virtual void	write (FileStorage &fs) const CV_OVERRIDE
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

Member Function Documentation

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

static

Python:
	retval	=	cv.face.EigenFaceRecognizer_create(	[, num_components[, 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.

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

• facerec.hpp