Abstract base class for all face recognition models. More...

#include <face.hpp>

Inherited by BasicFaceRecognizer, and LBPHFaceRecognizer.

Public Member Functions
virtual CV_WRAP void	train (InputArrayOfArrays src, InputArray labels)=0
	Trains a FaceRecognizer with given data and associated labels.
virtual CV_WRAP void	update (InputArrayOfArrays src, InputArray labels)
	Updates a FaceRecognizer with given data and associated labels.
	CV_WRAP_AS (predict_label) int predict(InputArray src) const
CV_WRAP void	predict (InputArray src, CV_OUT int &label, CV_OUT double &confidence) const
	Predicts a label and associated confidence (e.g.
	CV_WRAP_AS (predict_collect) virtual void predict(InputArray src
	if implemented - send all result of prediction to collector that can be used for somehow custom result handling
virtual CV_WRAP void	save (const String &filename) const
	Saves a FaceRecognizer and its model state.
virtual CV_WRAP void	load (const String &filename)
	Loads a FaceRecognizer and its model state.
virtual void	save (FileStorage &fs) const =0
	This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. Saves this model to a given FileStorage.
virtual void	load (const FileStorage &fs)=0
virtual CV_WRAP void	setLabelInfo (int label, const String &strInfo)
	Sets string info for the specified model's label.
virtual CV_WRAP String	getLabelInfo (int label) const
	Gets string information by label.
virtual CV_WRAP std::vector< int >	getLabelsByString (const String &str) const
	Gets vector of labels by string.
virtual double	getThreshold () const =0
	threshold parameter accessor - required for default BestMinDist collector
virtual void	setThreshold (double val)=0
	Sets threshold of model.
virtual CV_WRAP void	clear ()
	Clears the algorithm state.
virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage.
virtual void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage.
virtual bool	empty () const
	Returns true if the Algorithm is empty (e.g.
virtual CV_WRAP String	getDefaultName () const
	Returns the algorithm string identifier.
Static Public Member Functions
template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node.
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.

Detailed Description

Abstract base class for all face recognition models.

All face recognition models in OpenCV are derived from the abstract base class FaceRecognizer, which provides a unified access to all face recongition algorithms in OpenCV.

### Description

I'll go a bit more into detail explaining FaceRecognizer, because it doesn't look like a powerful interface at first sight. But: Every FaceRecognizer is an Algorithm, so you can easily get/set all model internals (if allowed by the implementation). Algorithm is a relatively new OpenCV concept, which is available since the 2.4 release. I suggest you take a look at its description.

Algorithm provides the following features for all derived classes:

So called “virtual constructor”. That is, each Algorithm derivative is registered at program start and you can get the list of registered algorithms and create instance of a particular algorithm by its name (see Algorithm::create). If you plan to add your own algorithms, it is good practice to add a unique prefix to your algorithms to distinguish them from other algorithms.
Setting/Retrieving algorithm parameters by name. If you used video capturing functionality from OpenCV highgui module, you are probably familar with cv::cvSetCaptureProperty, ocvcvGetCaptureProperty, VideoCapture::set and VideoCapture::get. Algorithm provides similar method where instead of integer id's you specify the parameter names as text Strings. See Algorithm::set and Algorithm::get for details.
Reading and writing parameters from/to XML or YAML files. Every Algorithm derivative can store all its parameters and then read them back. There is no need to re-implement it each time.

Moreover every FaceRecognizer supports the:

**Training** of a FaceRecognizer with FaceRecognizer::train on a given set of images (your face database!).
**Prediction** of a given sample image, that means a face. The image is given as a Mat.
**Loading/Saving** the model state from/to a given XML or YAML.
**Setting/Getting labels info**, that is stored as a string. String labels info is useful for keeping names of the recognized people.

Note:: When using the FaceRecognizer interface in combination with Python, please stick to Python 2. Some underlying scripts like create_csv will not work in other versions, like Python 3. Setting the Thresholds +++++++++++++++++++++++

Sometimes you run into the situation, when you want to apply a threshold on the prediction. A common scenario in face recognition is to tell, whether a face belongs to the training dataset or if it is unknown. You might wonder, why there's no public API in FaceRecognizer to set the threshold for the prediction, but rest assured: It's supported. It just means there's no generic way in an abstract class to provide an interface for setting/getting the thresholds of *every possible* FaceRecognizer algorithm. The appropriate place to set the thresholds is in the constructor of the specific FaceRecognizer and since every FaceRecognizer is a Algorithm (see above), you can get/set the thresholds at runtime!

Here is an example of setting a threshold for the Eigenfaces method, when creating the model:

// Let's say we want to keep 10 Eigenfaces and have a threshold value of 10.0
int num_components = 10;
double threshold = 10.0;
// Then if you want to have a cv::FaceRecognizer with a confidence threshold,
// create the concrete implementation with the appropiate parameters:
Ptr<FaceRecognizer> model = createEigenFaceRecognizer (num_components, threshold);

Sometimes it's impossible to train the model, just to experiment with threshold values. Thanks to Algorithm it's possible to set internal model thresholds during runtime. Let's see how we would set/get the prediction for the Eigenface model, we've created above:

// The following line reads the threshold from the Eigenfaces model:
double current_threshold = model->getDouble("threshold");
// And this line sets the threshold to 0.0:
model->set("threshold", 0.0);

If you've set the threshold to 0.0 as we did above, then:

//
Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE);
// Get a prediction from the model. Note: We've set a threshold of 0.0 above,
// since the distance is almost always larger than 0.0, you'll get -1 as
// label, which indicates, this face is unknown
int predicted_label = model->predict(img);
// ...

is going to yield -1 as predicted label, which states this face is unknown.

### Getting the name of a FaceRecognizer

Since every FaceRecognizer is a Algorithm, you can use Algorithm::name to get the name of a FaceRecognizer:

// Create a FaceRecognizer:
Ptr<FaceRecognizer> model = createEigenFaceRecognizer ();
// And here's how to get its name:
String name = model->name();

Definition at line 157 of file face.hpp.

Member Function Documentation

virtual CV_WRAP void clear ( ) [virtual, inherited]

Clears the algorithm state.

Definition at line 2984 of file core.hpp.

CV_WRAP_AS ( predict_collect )

if implemented - send all result of prediction to collector that can be used for somehow custom result handling

Parameters:

src	Sample image to get a prediction from.
collector	User-defined collector object that accepts all results

To implement this method u just have to do same internal cycle as in predict(InputArray src, CV_OUT int &label, CV_OUT double &confidence) but not try to get "best@ result, just resend it to caller side with given collector

CV_WRAP_AS ( predict_label ) const

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

virtual bool empty ( ) const [virtual, inherited]

Returns true if the Algorithm is empty (e.g.

in the very beginning or after unsuccessful read

Definition at line 2996 of file core.hpp.

String getDefaultName ( ) const [virtual, inherited]

Returns the algorithm string identifier.

This string is used as top level xml/yml node tag when the object is saved to a file or string.

Definition at line 65 of file algorithm.cpp.

String getLabelInfo ( int label ) const [virtual]

Gets string information by label.

If an unknown label id is provided or there is no label information associated with the specified label id the method returns an empty string.

Definition at line 38 of file facerec.cpp.

std::vector< int > getLabelsByString ( const String & str ) const [virtual]

Gets vector of labels by string.

The function searches for the labels containing the specified sub-string in the associated string info.

Definition at line 26 of file facerec.cpp.

virtual double getThreshold ( ) const [pure virtual]

threshold parameter accessor - required for default BestMinDist collector

virtual void load ( const FileStorage & fs ) [pure virtual]

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

static Ptr<_Tp> load	(	const String &	filename,
		const String &	objname = `String()`
	)		`[static, inherited]`

Loads algorithm from the file.

Parameters:

filename	Name of the file to read.
objname	The optional name of the node to read (if empty, the first top-level node will be used)

This is static template method of Algorithm. It's usage is following (in the case of SVM):

     Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");

In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn).

Definition at line 3027 of file core.hpp.

void load ( const String & filename ) [virtual]

Loads a FaceRecognizer and its model state.

Loads a persisted model and state from a given XML or YAML file . Every FaceRecognizer has to overwrite FaceRecognizer::load(FileStorage& fs) to enable loading the model state. FaceRecognizer::load(FileStorage& fs) in turn gets called by FaceRecognizer::load(const String& filename), to ease saving a model.

Definition at line 57 of file facerec.cpp.

static Ptr<_Tp> loadFromString	(	const String &	strModel,
		const String &	objname = `String()`
	)		`[static, inherited]`

Loads algorithm from a String.

Parameters:

strModel	The string variable containing the model you want to load.
objname	The optional name of the node to read (if empty, the first top-level node will be used)

This is static template method of Algorithm. It's usage is following (in the case of SVM):

     Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);

Definition at line 3046 of file core.hpp.

void predict	(	InputArray	src,
		CV_OUT int &	label,
		CV_OUT double &	confidence
	)		const

Predicts a label and associated confidence (e.g.

distance) for a given input image.

Parameters:

src	Sample image to get a prediction from.
label	The predicted label for the given image.
confidence	Associated confidence (e.g. distance) for the predicted label.

The suffix const means that prediction does not affect the internal model state, so the method can be safely called from within different threads.

The following example shows how to get a prediction from a trained model:

    using namespace cv;
    // Do your initialization here (create the cv::FaceRecognizer model) ...
    // ...
    // Read in a sample image:
    Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE);
    // And get a prediction from the cv::FaceRecognizer:
    int predicted = model->predict(img);

Or to get a prediction and the associated confidence (e.g. distance):

    using namespace cv;
    // Do your initialization here (create the cv::FaceRecognizer model) ...
    // ...
    Mat img = imread("person1/3.jpg", CV_LOAD_IMAGE_GRAYSCALE);
    // Some variables for the predicted label and associated confidence (e.g. distance):
    int predicted_label = -1;
    double predicted_confidence = 0.0;
    // Get the prediction and associated confidence from the model
    model->predict(img, predicted_label, predicted_confidence);

Definition at line 82 of file facerec.cpp.

virtual void read ( const FileNode & fn ) [virtual, inherited]

Reads algorithm parameters from a file storage.

Definition at line 2992 of file core.hpp.

static Ptr<_Tp> read ( const FileNode & fn ) [static, inherited]

Reads algorithm from the file node.

This is static template method of Algorithm. It's usage is following (in the case of SVM):

     Ptr<SVM> svm = Algorithm::read<SVM>(fn);

In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn) and also have static create() method without parameters (or with all the optional parameters)

Definition at line 3008 of file core.hpp.

virtual void save ( FileStorage & fs ) const [pure virtual]

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. Saves this model to a given FileStorage.

Parameters:

fs	The FileStorage to store this FaceRecognizer to.

void save ( const String & filename ) const [virtual]

Saves a FaceRecognizer and its model state.

Saves this model to a given filename, either as XML or YAML.

Parameters:

filename The filename to store this FaceRecognizer to (either XML/YAML).

Every FaceRecognizer overwrites FaceRecognizer::save(FileStorage& fs) to save the internal model state. FaceRecognizer::save(const String& filename) saves the state of a model to the given filename.

The suffix const means that prediction does not affect the internal model state, so the method can be safely called from within different threads.

Reimplemented from Algorithm.

Definition at line 66 of file facerec.cpp.

void setLabelInfo	(	int	label,
		const String &	strInfo
	)		`[virtual]`

Sets string info for the specified model's label.

The string info is replaced by the provided value if it was set before for the specified label.

Definition at line 44 of file facerec.cpp.

virtual void setThreshold ( double val ) [pure virtual]

Sets threshold of model.

virtual CV_WRAP void train	(	InputArrayOfArrays	src,
		InputArray	labels
	)		`[pure virtual]`

Trains a FaceRecognizer with given data and associated labels.

Parameters:

src	The training images, that means the faces you want to learn. The data has to be given as a vector<Mat>.
labels	The labels corresponding to the images have to be given either as a vector<int> or a

The following source code snippet shows you how to learn a Fisherfaces model on a given set of images. The images are read with imread and pushed into a std::vector<Mat>. The labels of each image are stored within a std::vector<int> (you could also use a Mat of type CV_32SC1). Think of the label as the subject (the person) this image belongs to, so same subjects (persons) should have the same label. For the available FaceRecognizer you don't have to pay any attention to the order of the labels, just make sure same persons have the same label:

    // holds images and labels
    vector<Mat> images;
    vector<int> labels;
    // images for first person
    images.push_back(imread("person0/0.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0);
    images.push_back(imread("person0/1.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0);
    images.push_back(imread("person0/2.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(0);
    // images for second person
    images.push_back(imread("person1/0.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1);
    images.push_back(imread("person1/1.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1);
    images.push_back(imread("person1/2.jpg", CV_LOAD_IMAGE_GRAYSCALE)); labels.push_back(1);

Now that you have read some images, we can create a new FaceRecognizer. In this example I'll create a Fisherfaces model and decide to keep all of the possible Fisherfaces:

    // Create a new Fisherfaces model and retain all available Fisherfaces,
    // this is the most common usage of this specific FaceRecognizer:
    //
    Ptr<FaceRecognizer> model =  createFisherFaceRecognizer ();

And finally train it on the given dataset (the face images and labels):

    // This is the common interface to train all of the available cv::FaceRecognizer
    // implementations:
    //
    model->train(images, labels);

void update	(	InputArrayOfArrays	src,
		InputArray	labels
	)		`[virtual]`

Updates a FaceRecognizer with given data and associated labels.

Parameters:

src	The training images, that means the faces you want to learn. The data has to be given as a vector<Mat>.
labels	The labels corresponding to the images have to be given either as a vector<int> or a

This method updates a (probably trained) FaceRecognizer, but only if the algorithm supports it. The Local Binary Patterns Histograms (LBPH) recognizer (see createLBPHFaceRecognizer) can be updated. For the Eigenfaces and Fisherfaces method, this is algorithmically not possible and you have to re-estimate the model with FaceRecognizer::train. In any case, a call to train empties the existing model and learns a new model, while update does not delete any model data.

    // Create a new LBPH model (it can be updated) and use the default parameters,
    // this is the most common usage of this specific FaceRecognizer:
    //
    Ptr<FaceRecognizer> model =  createLBPHFaceRecognizer ();
    // This is the common interface to train all of the available cv::FaceRecognizer
    // implementations:
    //
    model->train(images, labels);
    // Some containers to hold new image:
    vector<Mat> newImages;
    vector<int> newLabels;
    // You should add some images to the containers:
    //
    // ...
    //
    // Now updating the model is as easy as calling:
    model->update(newImages,newLabels);
    // This will preserve the old model data and extend the existing model
    // with the new features extracted from newImages!

Calling update on an Eigenfaces model (see createEigenFaceRecognizer), which doesn't support updating, will throw an error similar to:

    OpenCV Error: The function/feature is not implemented (This FaceRecognizer (FaceRecognizer.Eigenfaces) does not support updating, you have to use FaceRecognizer::train to update it.) in update, file /home/philipp/git/opencv/modules/contrib/src/facerec.cpp, line 305
    terminate called after throwing an instance of 'cv::Exception'

Note:: The FaceRecognizer does not store your training images, because this would be very memory intense and it's not the responsibility of te FaceRecognizer to do so. The caller is responsible for maintaining the dataset, he want to work with.

Definition at line 49 of file facerec.cpp.

virtual void write ( FileStorage & fs ) const [virtual, inherited]

Stores algorithm parameters in a file storage.

Definition at line 2988 of file core.hpp.

Type:	Program
Created:	26 Jul 2017
Imports:	3
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