OpenCV  3.4.19-dev
Open Source Computer Vision
Static Public Member Functions | List of all members
cv::face::EigenFaceRecognizer Class Reference

#include <opencv2/face/facerec.hpp>

Inheritance diagram for cv::face::EigenFaceRecognizer:
cv::face::BasicFaceRecognizer cv::face::FaceRecognizer cv::Algorithm

Static Public Member Functions

static Ptr< EigenFaceRecognizercreate (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. More...
template<typename _Tp >
static Ptr< _Tp > loadFromString (const String &strModel, const String &objname=String())
 Loads algorithm from a String. More...
template<typename _Tp >
static Ptr< _Tp > read (const FileNode &fn)
 Reads algorithm from the file node. More...

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::MatgetProjections () 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
- Public Member Functions inherited from cv::face::FaceRecognizer
virtual String getLabelInfo (int label) const
 Gets string information by label. More...
virtual std::vector< int > getLabelsByString (const String &str) const
 Gets vector of labels by string. More...
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. More...
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 read (const String &filename)
 Loads a FaceRecognizer and its model state. More...
virtual void setLabelInfo (int label, const String &strInfo)
 Sets string info for the specified model's label. More...
virtual void train (InputArrayOfArrays src, InputArray labels)=0
 Trains a FaceRecognizer with given data and associated labels. More...
virtual void update (InputArrayOfArrays src, InputArray labels)
 Updates a FaceRecognizer with given data and associated labels. More...
virtual void write (const String &filename) const
 Saves a FaceRecognizer and its model state. More...
- Public Member Functions inherited from cv::Algorithm
 Algorithm ()
virtual ~Algorithm ()
virtual void clear ()
 Clears the algorithm state. More...
virtual String getDefaultName () const
virtual void save (const String &filename) const
void write (FileStorage &fs, const String &name) const
void write (const Ptr< FileStorage > &fs, const String &name=String()) 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 
cv.face.EigenFaceRecognizer.create([, num_components[, threshold]]) -> retval
cv.face.EigenFaceRecognizer_create([, num_components[, threshold]]) -> retval
num_componentsThe 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.
thresholdThe threshold applied in the prediction.


  • 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: