FaceRecognizer

Objective-C

@interface FaceRecognizer : Algorithm

Swift

class FaceRecognizer : Algorithm

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 appropriate parameters: Ptr model = EigenFaceRecognizer::create(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”, IMREAD_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 model = EigenFaceRecognizer::create(); // And here’s how to get its name: String name = model->name();

Member of Face

Methods

  • 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.

    Declaration

    Objective-C

    - (nonnull NSString *)getLabelInfo:(int)label;

    Swift

    func getLabelInfo(label: Int32) -> String

  • Declaration

    Objective-C

    - (int)predict_label:(Mat*)src NS_SWIFT_NAME(predict(src:));

    Swift

    func predict(src: Mat) -> Int32

  • Gets vector of labels by string.

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

    Declaration

    Objective-C

    - (nonnull IntVector *)getLabelsByString:(nonnull NSString *)str;

    Swift

    func getLabelsByString(str: String) -> IntVector
    • if implemented - send all result of prediction to collector that can be used for somehow custom result handling

    Declaration

    Objective-C

    - (void)predict_collect:(nonnull Mat *)src
              collector:(nonnull PredictCollector *)collector;

    Swift

    func predict(src: Mat, collector: PredictCollector)

    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

  • Predicts a label and associated confidence (e.g. distance) for a given input image.

    Declaration

    Objective-C

    - (void)predict:(nonnull Mat *)src
          label:(nonnull int *)label
     confidence:(nonnull double *)confidence;

    Swift

    func predict(src: Mat, label: UnsafeMutablePointer<Int32>, confidence: UnsafeMutablePointer<Double>)

    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”, IMREAD_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”, IMREAD_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);

  • 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.

    Declaration

    Objective-C

    - (void)read:(nonnull NSString *)filename;

    Swift

    func read(filename: String)

  • 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.

    Declaration

    Objective-C

    - (void)setLabelInfo:(int)label strInfo:(nonnull NSString *)strInfo;

    Swift

    func setLabelInfo(label: Int32, strInfo: String)

  • Trains a FaceRecognizer with given data and associated labels.

    Declaration

    Objective-C

    - (void)train:(nonnull NSArray<Mat *> *)src labels:(nonnull Mat *)labels;

    Swift

    func train(src: [Mat], labels: Mat)

    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 Mat of type CV_32SC1.

    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 images; vector labels; // using Mat of type CV_32SC1 // Mat labels(number_of_samples, 1, CV_32SC1); // images for first person images.push_back(imread(“person0/0.jpg”, IMREAD_GRAYSCALE)); labels.push_back(0); images.push_back(imread(“person0/1.jpg”, IMREAD_GRAYSCALE)); labels.push_back(0); images.push_back(imread(“person0/2.jpg”, IMREAD_GRAYSCALE)); labels.push_back(0); // images for second person images.push_back(imread(“person1/0.jpg”, IMREAD_GRAYSCALE)); labels.push_back(1); images.push_back(imread(“person1/1.jpg”, IMREAD_GRAYSCALE)); labels.push_back(1); images.push_back(imread(“person1/2.jpg”, IMREAD_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 model = FisherFaceRecognizer::create();

    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);

  • Updates a FaceRecognizer with given data and associated labels.

    Declaration

    Objective-C

    - (void)update:(nonnull NSArray<Mat *> *)src labels:(nonnull Mat *)labels;

    Swift

    func update(src: [Mat], labels: Mat)

    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 Mat of type CV_32SC1.

    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 model = LBPHFaceRecognizer::create(); // 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 newImages; vector 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 EigenFaceRecognizer::create), 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.

  • Saves a FaceRecognizer and its model state.

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

    Declaration

    Objective-C

    - (void)write:(nonnull NSString *)filename;

    Swift

    func write(filename: String)

    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.