cv::face::FisherFaceRecognizer Class Reference

#include <opencv2/face/facerec.hpp>

Inheritance diagram for cv::face::FisherFaceRecognizer:

Static Public Member Functions
static Ptr< FisherFaceRecognizer >	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<FisherFaceRecognizer> cv::face::FisherFaceRecognizer::create ( int  num_components = 0, double  threshold = DBL_MAX  )

static

Python:
	retval	=	cv.face.FisherFaceRecognizer_create(	[, num_components[, threshold]]	)

Parameters

num_components	The number of components (read: Fisherfaces) kept for this Linear Discriminant Analysis with the Fisherfaces criterion. It's useful to keep all components, that means the number of your classes c (read: subjects, persons you want to recognize). If you leave this at the default (0) or set it to a value less-equal 0 or greater (c-1), it will be set to the correct number (c-1) automatically.
threshold	The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.

Notes:

• Training and prediction must be done on grayscale images, use cvtColor to convert between the color spaces.
• THE FISHERFACES 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 FisherFaceRecognizer::create.
• threshold see FisherFaceRecognizer::create.
• eigenvalues The eigenvalues for this Linear Discriminant Analysis (ordered descending).
• eigenvectors The eigenvectors for this Linear Discriminant Analysis (ordered by their eigenvalue).
• mean The sample mean calculated from the training data.
• projections The projections of the training data.
• labels The labels corresponding to the projections.

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

• facerec.hpp