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+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+// Copyright (c) 2011,2012. Philipp Wagner <bytefish[at]gmx[dot]de>.
+// Third party copyrights are property of their respective owners.
+
+#ifndef __OPENCV_FACEREC_HPP__
+#define __OPENCV_FACEREC_HPP__
+
+#include "opencv2/face.hpp"
+#include "opencv2/core.hpp"
+
+namespace cv { namespace face {
+
+//! @addtogroup face
+//! @{
+
+// base for two classes
+class CV_EXPORTS_W BasicFaceRecognizer : public FaceRecognizer
+{
+public:
+    /** @see setNumComponents */
+    CV_WRAP virtual int getNumComponents() const = 0;
+    /** @copybrief getNumComponents @see getNumComponents */
+    CV_WRAP virtual void setNumComponents(int val) = 0;
+    /** @see setThreshold */
+    CV_WRAP virtual double getThreshold() const = 0;
+    /** @copybrief getThreshold @see getThreshold */
+    CV_WRAP virtual void setThreshold(double val) = 0;
+    CV_WRAP virtual std::vector<cv::Mat> getProjections() const = 0;
+    CV_WRAP virtual cv::Mat getLabels() const = 0;
+    CV_WRAP virtual cv::Mat getEigenValues() const = 0;
+    CV_WRAP virtual cv::Mat getEigenVectors() const = 0;
+    CV_WRAP virtual cv::Mat getMean() const = 0;
+};
+
+/**
+@param 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.
+@param 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 createEigenFaceRecognizer.
+-   threshold see createEigenFaceRecognizer.
+-   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.
+ */
+CV_EXPORTS_W Ptr<BasicFaceRecognizer> createEigenFaceRecognizer(int num_components = 0, double threshold = DBL_MAX);
+
+/**
+@param 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.
+@param 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 createFisherFaceRecognizer.
+-   threshold see createFisherFaceRecognizer.
+-   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.
+ */
+CV_EXPORTS_W Ptr<BasicFaceRecognizer> createFisherFaceRecognizer(int num_components = 0, double threshold = DBL_MAX);
+
+class CV_EXPORTS_W LBPHFaceRecognizer : public FaceRecognizer
+{
+public:
+    /** @see setGridX */
+    CV_WRAP virtual int getGridX() const = 0;
+    /** @copybrief getGridX @see getGridX */
+    CV_WRAP virtual void setGridX(int val) = 0;
+    /** @see setGridY */
+    CV_WRAP virtual int getGridY() const = 0;
+    /** @copybrief getGridY @see getGridY */
+    CV_WRAP virtual void setGridY(int val) = 0;
+    /** @see setRadius */
+    CV_WRAP virtual int getRadius() const = 0;
+    /** @copybrief getRadius @see getRadius */
+    CV_WRAP virtual void setRadius(int val) = 0;
+    /** @see setNeighbors */
+    CV_WRAP virtual int getNeighbors() const = 0;
+    /** @copybrief getNeighbors @see getNeighbors */
+    CV_WRAP virtual void setNeighbors(int val) = 0;
+    /** @see setThreshold */
+    CV_WRAP virtual double getThreshold() const = 0;
+    /** @copybrief getThreshold @see getThreshold */
+    CV_WRAP virtual void setThreshold(double val) = 0;
+    CV_WRAP virtual std::vector<cv::Mat> getHistograms() const = 0;
+    CV_WRAP virtual cv::Mat getLabels() const = 0;
+};
+
+/**
+@param radius The radius used for building the Circular Local Binary Pattern. The greater the
+radius, the
+@param neighbors The number of sample points to build a Circular Local Binary Pattern from. An
+appropriate value is to use `8` sample points. Keep in mind: the more sample points you include,
+the higher the computational cost.
+@param grid_x The number of cells in the horizontal direction, 8 is a common value used in
+publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
+feature vector.
+@param grid_y The number of cells in the vertical direction, 8 is a common value used in
+publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
+feature vector.
+@param threshold The threshold applied in the prediction. If the distance to the nearest neighbor
+is larger than the threshold, this method returns -1.
+
+### Notes:
+
+-   The Circular Local Binary Patterns (used in training and prediction) expect the data given as
+    grayscale images, use cvtColor to convert between the color spaces.
+-   This model supports updating.
+
+### Model internal data:
+
+-   radius see createLBPHFaceRecognizer.
+-   neighbors see createLBPHFaceRecognizer.
+-   grid_x see createLBPHFaceRecognizer.
+-   grid_y see createLBPHFaceRecognizer.
+-   threshold see createLBPHFaceRecognizer.
+-   histograms Local Binary Patterns Histograms calculated from the given training data (empty if
+    none was given).
+-   labels Labels corresponding to the calculated Local Binary Patterns Histograms.
+ */
+CV_EXPORTS_W Ptr<LBPHFaceRecognizer> createLBPHFaceRecognizer(int radius=1, int neighbors=8, int grid_x=8, int grid_y=8, double threshold = DBL_MAX);
+
+//! @}
+
+}} //namespace cv::face
+
+#endif //__OPENCV_FACEREC_HPP__