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+Face landmark detection in an image {#tutorial_face_landmark_detection_in_an_image}
+===================================
+
+![](images/facereg.jpg)
+
+This application lets you detect landmarks of detected faces in an image. You can detect landmarks of all the faces found in an image
+and use them further in various applications like face swapping, face averaging etc.
+This functionality is now available in OpenCV.
+
+```
+// Command to be typed for running the sample
+./sampleDetectLandmarks -file=trained_model.dat -face_cascade=lbpcascadefrontalface.xml -image=/path_to_image/image.jpg
+```
+### Description of command parameters {tutorial_face_training_parameters}
+
+> * **model_filename** f : (REQUIRED) A path to binary file storing the trained model which is to be loaded [example - /data/file.dat]
+> * **image** i : (REQUIRED) A path to image in which face landmarks have to be detected.[example - /data/image.jpg]
+> * **face_cascade** c : (REQUIRED) A path to the face cascade xml file which you want to use as a face detector.
+
+Understanding code
+------------------
+
+![](images/d.png)
+
+This tutorial will explain the sample code for face landmark detection. Jumping directly to the code :
+
+``` c++
+CascadeClassifier face_cascade;
+face_cascade.load(cascade_name);
+
+Mat img = imread(image);
+Ptr<Facemark> facemark = createFacemarkKazemi());
+facemark->loadModel(filename);
+cout<<"Loaded model"<<endl;
+```
+
+The above code creates a CascadeClassifier to detect face regions, and an instance of the face landmark detection class.
+We need to load a pretrained model for face landmark detection, and a cascade file for the face detection.
+It also loads the image in which landmarks have to be detected.
+
+
+``` c++
+vector<Rect> faces;
+resize(img,img,Size(460,460),0,0,INTER_LINEAR_EXACT);
+
+Mat gray;
+std::vector<Rect> faces;
+if(img.channels()>1){
+    cvtColor(img.getMat(),gray,COLOR_BGR2GRAY);
+}
+else{
+    gray = img.getMat().clone();
+}
+equalizeHist( gray, gray );
+
+face_cascade.detectMultiScale( gray, faces, 1.1, 3,0, Size(30, 30) );
+```
+
+After doing some preprocessing, we first have to detect possible face regions (which will be stored in a `vector<Rect>`.
+Also, the image is resized to a smaller size as processing speed is faster with small images.
+
+
+``` c++
+vector< vector<Point2f> > shapes;
+
+if (facemark->fit(img,faces,shapes))
+{
+    for ( size_t i = 0; i < faces.size(); i++ )
+    {
+        cv::rectangle(img,faces[i],Scalar( 255, 0, 0 ));
+    }
+    for (unsigned long i=0;i<faces.size();i++){
+        for(unsigned long k=0;k<shapes[i].size();k++)
+            cv::circle(img,shapes[i][k],5,cv::Scalar(0,0,255),FILLED);
+    }
+    namedWindow("Detected_shape");
+    imshow("Detected_shape",img);
+    waitKey(0);
+}
+```
+
+
+It then creates a vector of vector to store shapes for each face detected.
+The above code calls the function fit to get shapes of all detected faces in the image
+and then draws the rectangles bounding the faces and marks the desired landmarks.
+
+### Detection Results
+
+![](ab.jpg)
+
+![](ab-1.jpg)
@@ -0,0 +1,177 @@
+![](face_landmark/images/2.jpg)
+
+Training face landmark detector{#tutorial_face_training_face_landmark_detector}
+==============================
+
+This application helps to train your own face landmark detector. You can train your own face landmark detection by just providing the paths for
+directory containing the images and files containing their corresponding face landmarks. As this landmark detector was originally trained on
+[HELEN dataset](http://www.ifp.illinois.edu/~vuongle2/helen/), the training follows the format of data provided in HELEN dataset.
+
+The dataset consists of .txt files whose first line contains the image name which then follows the annotations.
+The format of the file containing annotations should be of following format :
+>       /directory/images/abc.jpg
+>       123.45,345.65
+>       321.67,543.89
+>       .... , ....
+>       .... , ....
+The above format is similar to HELEN dataset which is used for training the model.
+
+```
+// Command to be typed for running the sample
+./sample_train_landmark_detector -annotations=/home/sukhad/Downloads/code/trainset/ -config=config.xml -face_cascade=lbpcascadefrontalface.xml -model=trained_model.dat -width=460 -height=460
+```
+
+## Description of command parameters
+
+> * **annotations** a : (REQUIRED) Path to annotations txt file [example - /data/annotations.txt]
+> * **config** c : (REQUIRED) Path to configuration xml file containing parameters for training.[ example - /data/config.xml]
+> * **model** m :  (REQUIRED) Path to configuration xml file containing parameters for training.[ example - /data/model.dat]
+> * **width** w : (OPTIONAL)  The width which you want all images to get to scale the annotations. Large images are slow to process [default = 460]
+> * **height** h : (OPTIONAL) The height which you want all images to get to scale the annotations. Large images are slow to process [default = 460]
+> * **face_cascade** f (REQUIRED) Path to the face cascade xml file which you want to use as a detector.
+
+## Description of training parameters
+
+
+The configuration file described above which is used while training contains the training parameters which are required for training.
+
+**The description of parameters is as follows :**
+
+1. **Cascade depth :** This stores the depth of cascade of regressors used for training.
+2. **Tree depth :** This stores the depth of trees created as weak learners during gradient boosting.
+3. **Number of trees per cascade level :** This stores number of trees required per cascade level.
+4. **Learning rate :** This stores the learning rate for gradient boosting.This is required to prevent overfitting using shrinkage.
+5. **Oversampling amount :** This stores the oversampling amount for the samples.
+6. **Number of test coordinates :** This stores number of test coordinates to be generated as samples to decide for making the split.
+7. **Lambda :** This stores the value used for calculating the probabilty which helps to select closer pixels for making the split.
+8. **Number of test splits :** This stores the number of test splits to be generated before making the best split.
+
+
+To get more detailed description about the training parameters you can refer to the [Research paper](https://pdfs.semanticscholar.org/d78b/6a5b0dcaa81b1faea5fb0000045a62513567.pdf).
+
+## Understanding code
+
+
+![](face_landmark/images/3.jpg)
+
+
+Jumping directly to the code :
+
+``` c++
+CascadeClassifier face_cascade;
+bool myDetector( InputArray image, OutputArray ROIs );
+
+bool myDetector( InputArray image, OutputArray ROIs ){
+    Mat gray;
+    std::vector<Rect> faces;
+    if(image.channels()>1){
+        cvtColor(image.getMat(),gray,COLOR_BGR2GRAY);
+    }
+    else{
+        gray = image.getMat().clone();
+    }
+    equalizeHist( gray, gray );
+    face_cascade.detectMultiScale( gray, faces, 1.1, 3,0, Size(30, 30) );
+    Mat(faces).copyTo(ROIs);
+    return true;
+}
+```
+The facemark API provides the functionality to the user to use their own face detector to be used in training.The above code creartes a sample face detector. The above function would be passed to a function pointer in the facemark API.
+
+``` c++
+vector<String> filenames;
+glob(directory,filenames);
+```
+The above code creates a vector filenames for storing the names of the .txt files.
+It gets the filenames of the files in the directory.
+
+``` c++
+Mat img = imread(image);
+face_cascade.load(cascade_name);
+FacemarkKazemi::Params params;
+params.configfile = configfile_name;
+Ptr<Facemark> facemark = FacemarkKazemi::create(params);
+facemark->setFaceDetector(myDetector);
+
+```
+The above code creates a pointer of the face landmark detection class. The face detector created above has to be passed
+as function pointer to the facemark pointer created for detecting faces while training the model.
+
+``` c++
+vector<String> imagenames;
+vector< vector<Point2f> > trainlandmarks,Trainlandmarks;
+vector<Mat> trainimages;
+loadTrainingData(filenames,trainlandmarks,imagenames);
+for(unsigned long i=0;i<300;i++){
+string imgname = imagenames[i].substr(0, imagenames[i].size()-1);
+string img = directory + string(imgname) + ".jpg";
+Mat src = imread(img);
+if(src.empty()){
+    cerr<<string("Image "+img+" not found\n.")<<endl;
+    continue;
+}
+trainimages.push_back(src);
+Trainlandmarks.push_back(trainlandmarks[i]);
+}
+```
+The above code creates std::vectors to store the images and their corresponding landmarks.
+The above code calls a function loadTrainingData to load the landmarks and the images into their respective vectors.
+
+If the dataset you downloaded is of the following format :
+```
+version: 1
+n_points:  68
+{
+ 115.167660 220.807529
+ 116.164839 245.721357
+ 120.208690 270.389841
+  ...
+}
+This is the example of the dataset available at https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/
+
+```
+
+Then skip the above code for loading training data and use the following code. This sample is provided as sampleTrainLandmarkDetector2.cpp
+in the face module in opencv contrib.
+
+``` c++
+std::vector<String> images;
+std::vector<std::vector<Point2f> > facePoints;
+loadTrainingData(imagesList, annotations, images, facePoints, 0.0);
+```
+
+In the above code imagelist and annotations are the file of following format :
+```
+example of contents for images.txt:
+../trainset/image_0001.png
+../trainset/image_0002.png
+example of contents for annotation.txt:
+../trainset/image_0001.pts
+../trainset/image_0002.pts
+```
+
+These symbolize the names of images and their corresponding annotations.
+
+The above code scales images and landmarks as training on images of smaller size takes less time.
+This is because processing larger images requires more time. After scaling data it calculates mean
+shape of the data which is used as initial shape while training.
+
+Finally call the following function to perform training :
+
+``` c++
+facemark->training(Trainimages,Trainlandmarks,configfile_name,scale,modelfile_name);
+```
+In the above function scale is passed to scale all images and the corresponding landmarks so that the size of all
+images can be reduced as it takes greater time to process large images.
+This call to the train function trains the model and stores the trained model file with the given
+filename specified.As the training starts successfully you will see something like this :
+![](face_landmark/images/train1.png)
+
+
+**The error rate on trained images depends on the number of images used for training used as follows :**
+
+![](face_landmark/images/train.png)
+
+**The error rate on test images depends on the number of images used for training used as follows :**
+
+![](face_landmark/images/test.png)
@@ -0,0 +1,110 @@
+Face landmark detection in a video{#tutorial_face_landmark_detection_in_video}
+===================================
+
+This application lets you detect landmarks of detected faces in a video.This application first detects faces in a current video frame
+and then finds their facial landmarks. You just have to pass the video as input.
+```
+// Command to be typed for running the sample
+./sampleDetectLandmarks -file=trained_model.dat -face_cascade=lbpcascadefrontalface.xml -video=/path_to_video/video.avi
+```
+Description of command parameters
+---------------------------------
+
+> * **model_filename** f : (REQUIRED) A path to binary file storing the trained model which is to be loaded [example - /data/file.dat]
+> * **video** v : (REQUIRED) A path to video in which face landmarks have to be detected.[example - /data/video.avi]
+> * **face_cascade** c : (REQUIRED) A path to the face cascade xml file which you want to use as a face detector.
+
+### Understanding code
+
+This tutorial will explain the sample code for face landmark detection. Jumping directly to the code :
+
+``` c++
+CascadeClassifier face_cascade;
+bool myDetector( InputArray image, OutputArray ROIs );
+
+bool myDetector( InputArray image, OutputArray ROIs ){
+    Mat gray;
+    std::vector<Rect> faces;
+    if(image.channels()>1){
+        cvtColor(image.getMat(),gray,COLOR_BGR2GRAY);
+    }
+    else{
+        gray = image.getMat().clone();
+    }
+    equalizeHist( gray, gray );
+    face_cascade.detectMultiScale( gray, faces, 1.1, 3,0, Size(30, 30) );
+    Mat(faces).copyTo(ROIs);
+    return true;
+}
+```
+The facemark API provides the functionality to the user to use their own face detector to be used in face landmark detection.The above code creartes a sample face detector. The above function would be passed to a function pointer in the facemark API.
+
+``` c++
+VideoCapture cap(video);
+if(!cap.isOpened()){
+	cerr<<"Video cannot be loaded. Give correct path"<<endl;
+	return -1;
+}
+```
+
+The above code creates a video capture object and then loads the video.
+If the video is not loaded properly it prompts the user else the code proceeds.
+
+``` c++
+Mat img = imread(image);
+face_cascade.load(cascade_name);
+FacemarkKazemi::Params params;
+params.configfile = configfile_name;
+Ptr<Facemark> facemark = FacemarkKazemi::create(params);
+facemark->setFaceDetector(myDetector);
+
+```
+The above code creates a pointer of the face landmark detection class. The face detector created above has to be passed
+as function pointer to the facemark pointer created for detecting faces.
+``` c++
+vector<Rect> faces;
+vector< vector<Point2f> > shapes;
+Mat img;
+```
+The above code creates a vector to store the detected faces and a vector of vector to store shapes for each
+face detected in the current frame.
+
+``` c++
+while(1){
+	faces.clear();
+	shapes.clear();
+	cap>>img;
+	resize(img,img,Size(600,600),0,0,INTER_LINEAR_EXACT);
+	facemark->getFaces(img,faces);
+	if(faces.size()==0){
+	    cout<<"No faces found in this frame"<<endl;
+	}
+	else{
+	    for( size_t i = 0; i < faces.size(); i++ )
+	    {
+		cv::rectangle(img,faces[i],Scalar( 255, 0, 0 ));
+	    }
+	    if(facemark->fit(img,faces,shapes))
+	    {
+		for(unsigned long i=0;i<faces.size();i++){
+		    for(unsigned long k=0;k<shapes[i].size();k++)
+		        cv::circle(img,shapes[i][k],3,cv::Scalar(0,0,255),FILLED);
+		}
+	    }
+	}
+	namedWindow("Detected_shape");
+	imshow("Detected_shape",img);
+	if(waitKey(1) >= 0) break;
+}
+```
+
+The above code then reads each frame and detects faces and the landmarks corresponding to each shape detected.
+It then displays the current frame.
+
+After running the above code you will get results something like this
+
+Sample video:
+
+@htmlonly
+<iframe width="560" height="315" src="https://www.youtube.com/embed/ZtaV07T90D8" frameborder="0" allowfullscreen></iframe>
+@endhtmlonly
@@ -0,0 +1,147 @@
+Face swapping using face landmark detection{#tutorial_face_swapping_face_landmark_detection}
+===========================================
+
+This application lets you swap a face in one image with another face in other image. The application first detects faces in both images and finds its landmarks. Then it swaps the face in first image with in another image. You just have to give paths to the images run the application to swap the two faces.
+```
+// Command to be typed for running the sample
+./sample_face_swapping -file=trained_model.dat -face_cascade=lbpcascadefrontalface.xml -image1=/path_to_image/image1.jpg -image2=/path_to_image/image2.jpg
+```
+### Description of command parameters
+
+> * **image1** i1 (REQUIRED) Path to the first image file in which you want to apply swapping.
+> * **image2** i2 (REQUIRED) Path to the second image file in which you want to apply face swapping.
+> * **model** m (REQUIRED) Path to the file containing model to be loaded for face landmark detection.
+> * **face_cascade** f (REQUIRED) Path to the face cascade xml file which you want to use as a face detector.
+
+### Understanding the code
+
+This tutorial will explain the sample code for face swapping using OpenCV. Jumping directly to the code :
+
+``` c++
+CascadeClassifier face_cascade;
+bool myDetector( InputArray image, OutputArray ROIs );
+
+bool myDetector( InputArray image, OutputArray ROIs ){
+    Mat gray;
+    std::vector<Rect> faces;
+    if(image.channels()>1){
+        cvtColor(image.getMat(),gray,COLOR_BGR2GRAY);
+    }
+    else{
+        gray = image.getMat().clone();
+    }
+    equalizeHist( gray, gray );
+    face_cascade.detectMultiScale( gray, faces, 1.1, 3,0, Size(30, 30) );
+    Mat(faces).copyTo(ROIs);
+    return true;
+}
+```
+The facemark API provides the functionality to the user to use their own face detector to be used in face landmark detection.The above code creartes a sample face detector. The above function would be passed to a function pointer in the facemark API.
+
+
+``` c++
+Mat img = imread(image);
+face_cascade.load(cascade_name);
+FacemarkKazemi::Params params;
+params.configfile = configfile_name;
+Ptr<Facemark> facemark = FacemarkKazemi::create(params);
+facemark->setFaceDetector(myDetector);
+```
+The above code creates a pointer of the face landmark detection class. The face detector created above has to be passed
+as function pointer to the facemark pointer created for detecting faces while training the model.
+``` c++
+vector<Rect> faces1,faces2;
+vector< vector<Point2f> > shape1,shape2;
+float ratio1 = (float)img1.cols/(float)img1.rows;
+float ratio2 = (float)img2.cols/(float)img2.rows;
+resize(img1,img1,Size(640*ratio1,640*ratio1),0,0,INTER_LINEAR_EXACT);
+resize(img2,img2,Size(640*ratio2,640*ratio2),0,0,INTER_LINEAR_EXACT);
+Mat img1Warped = img2.clone();
+facemark->getFaces(img1,faces1);
+facemark->getFaces(img2,faces2);
+facemark->fit(img1,faces1,shape1);
+facemark->fit(img2,faces2,shape2);
+
+```
+
+The above code creates vectors to store the detected faces and a vector of vector to store shapes for each
+face detected in both the images.It then detects landmarks of each face detected in both the images.the images are resized
+as it is easier to process small images. The images are resized according their actual ratio.
+
+
+``` c++
+vector<Point2f> boundary_image1;
+vector<Point2f> boundary_image2;
+vector<int> index;
+convexHull(Mat(points2),index, false, false);
+for(size_t i = 0; i < index.size(); i++)
+{
+    boundary_image1.push_back(points1[index[i]]);
+    boundary_image2.push_back(points2[index[i]]);
+}
+```
+
+The above code then finds convex hull to find the boundary points of the face in the image which has to be swapped.
+
+``` c++
+vector< vector<int> > triangles;
+Rect rect(0, 0, img1Warped.cols, img1Warped.rows);
+divideIntoTriangles(rect, boundary_image2, triangles);
+for(size_t i = 0; i < triangles.size(); i++)
+{
+    vector<Point2f> triangle1, triangle2;
+    for(int j = 0; j < 3; j++)
+    {
+        triangle1.push_back(boundary_image1[triangles[i][j]]);
+        triangle2.push_back(boundary_image2[triangles[i][j]]);
+    }
+    warpTriangle(img1, img1Warped, triangle1, triangle2);
+}
+```
+
+Now as we need to warp one face over the other and we need to find affine transform.
+Now as the function in OpenCV to find affine transform requires three set of points to calculate
+the affine matrix. Also we just need to warp the face instead of the surrounding regions. Hence
+we divide the face into triangles so that each triiangle can be easily warped onto the other image.
+
+The function divideIntoTriangles divides the detected faces into triangles.
+The function warpTriangle then warps each triangle of one image to other image  to swap the faces.
+
+``` c++
+vector<Point> hull;
+for(size_t i = 0; i < boundary_image2.size(); i++)
+{
+    Point pt((int)boundary_image2[i].x,(int)boundary_image2[i].y);
+    hull.push_back(pt);
+}
+Mat mask = Mat::zeros(img2.rows, img2.cols, img2.depth());
+fillConvexPoly(mask,&hull[0],(int)hull.size(), Scalar(255,255,255));
+Rect r = boundingRect(boundary_image2);
+Point center = (r.tl() + r.br()) / 2;
+Mat output;
+img1Warped.convertTo(img1Warped, CV_8UC3);
+seamlessClone(img1Warped,img2, mask, center, output, NORMAL_CLONE);
+imshow("Face_Swapped", output);
+```
+
+Even after warping the results somehow look unnatural. Hence to improve the results we apply seamless cloning
+to get the desired results as required.
+
+### Results
+
+Consider two images to be used for face swapping as follows :
+
+First image
+-----------
+
+![](images/227943776_1.jpg)
+
+Second image
+------------
+
+![](images/230501201_1.jpg)
+
+Results after swapping
+----------------------
+
+![](images/face_swapped.jpg)
@@ -0,0 +1,699 @@
+Face Recognition with OpenCV {#tutorial_face_main}
+============================
+
+[TOC]
+
+Introduction {#tutorial_face_intro}
+============
+
+[OpenCV (Open Source Computer Vision)](http://opencv.org) is a popular computer vision library
+started by [Intel](http://www.intel.com) in 1999. The cross-platform library sets its focus on
+real-time image processing and includes patent-free implementations of the latest computer vision
+algorithms. In 2008 [Willow Garage](http://www.willowgarage.com) took over support and OpenCV 2.3.1
+now comes with a programming interface to C, C++, [Python](http://www.python.org) and
+[Android](http://www.android.com). OpenCV is released under a BSD license so it is used in academic
+projects and commercial products alike.
+
+OpenCV 2.4 now comes with the very new FaceRecognizer class for face recognition, so you can start
+experimenting with face recognition right away. This document is the guide I've wished for, when I
+was working myself into face recognition. It shows you how to perform face recognition with
+FaceRecognizer in OpenCV (with full source code listings) and gives you an introduction into the
+algorithms behind. I'll also show how to create the visualizations you can find in many
+publications, because a lot of people asked for.
+
+The currently available algorithms are:
+
+-   Eigenfaces (see EigenFaceRecognizer::create)
+-   Fisherfaces (see FisherFaceRecognizer::create)
+-   Local Binary Patterns Histograms (see LBPHFaceRecognizer::create)
+
+You don't need to copy and paste the source code examples from this page, because they are available
+in the src folder coming with this documentation. If you have built OpenCV with the samples turned
+on, chances are good you have them compiled already! Although it might be interesting for very
+advanced users, I've decided to leave the implementation details out as I am afraid they confuse new
+users.
+
+All code in this document is released under the [BSD
+license](http://www.opensource.org/licenses/bsd-license), so feel free to use it for your projects.
+
+Face Recognition {#tutorial_face_facerec}
+----------------
+
+Face recognition is an easy task for humans. Experiments in @cite Tu06 have shown, that even one to
+three day old babies are able to distinguish between known faces. So how hard could it be for a
+computer? It turns out we know little about human recognition to date. Are inner features (eyes,
+nose, mouth) or outer features (head shape, hairline) used for a successful face recognition? How do
+we analyze an image and how does the brain encode it? It was shown by [David
+Hubel](http://en.wikipedia.org/wiki/David_H._Hubel) and [Torsten
+Wiesel](http://en.wikipedia.org/wiki/Torsten_Wiesel), that our brain has specialized nerve cells
+responding to specific local features of a scene, such as lines, edges, angles or movement. Since we
+don't see the world as scattered pieces, our visual cortex must somehow combine the different
+sources of information into useful patterns. Automatic face recognition is all about extracting
+those meaningful features from an image, putting them into a useful representation and performing
+some kind of classification on them.
+
+Face recognition based on the geometric features of a face is probably the most intuitive approach
+to face recognition. One of the first automated face recognition systems was described in
+@cite Kanade73 : marker points (position of eyes, ears, nose, ...) were used to build a feature vector
+(distance between the points, angle between them, ...). The recognition was performed by calculating
+the euclidean distance between feature vectors of a probe and reference image. Such a method is
+robust against changes in illumination by its nature, but has a huge drawback: the accurate
+registration of the marker points is complicated, even with state of the art algorithms. Some of the
+latest work on geometric face recognition was carried out in @cite Bru92 . A 22-dimensional feature
+vector was used and experiments on large datasets have shown, that geometrical features alone may not
+carry enough information for face recognition.
+
+The Eigenfaces method described in @cite TP91 took a holistic approach to face recognition: A facial
+image is a point from a high-dimensional image space and a lower-dimensional representation is
+found, where classification becomes easy. The lower-dimensional subspace is found with Principal
+Component Analysis, which identifies the axes with maximum variance. While this kind of
+transformation is optimal from a reconstruction standpoint, it doesn't take any class labels into
+account. Imagine a situation where the variance is generated from external sources, let it be light.
+The axes with maximum variance do not necessarily contain any discriminative information at all,
+hence a classification becomes impossible. So a class-specific projection with a Linear Discriminant
+Analysis was applied to face recognition in @cite BHK97 . The basic idea is to minimize the variance
+within a class, while maximizing the variance between the classes at the same time.
+
+Recently various methods for a local feature extraction emerged. To avoid the high-dimensionality of
+the input data only local regions of an image are described, the extracted features are (hopefully)
+more robust against partial occlusion, illumation and small sample size. Algorithms used for a local
+feature extraction are Gabor Wavelets (@cite Wiskott97), Discrete Cosinus Transform (@cite Messer06) and
+Local Binary Patterns (@cite AHP04). It's still an open research question what's the best way to
+preserve spatial information when applying a local feature extraction, because spatial information
+is potentially useful information.
+
+Face Database  {#tutorial_face_facedb}
+-------------
+
+Let's get some data to experiment with first. I don't want to do a toy example here. We are doing
+face recognition, so you'll need some face images! You can either create your own dataset or start
+with one of the available face databases,
+[<http://face-rec.org/databases/>](http://face-rec.org/databases) gives you an up-to-date overview.
+Three interesting databases are (parts of the description are quoted from
+[<http://face-rec.org>](http://face-rec.org)):
+
+-   [AT&T Facedatabase](http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html) The AT&T
+    Facedatabase, sometimes also referred to as *ORL Database of Faces*, contains ten different
+    images of each of 40 distinct subjects. For some subjects, the images were taken at different
+    times, varying the lighting, facial expressions (open / closed eyes, smiling / not smiling) and
+    facial details (glasses / no glasses). All the images were taken against a dark homogeneous
+    background with the subjects in an upright, frontal position (with tolerance for some side
+    movement).
+-   [Yale Facedatabase A](http://vision.ucsd.edu/content/yale-face-database), also known as
+    Yalefaces. The AT&T Facedatabase is good for initial tests, but it's a fairly easy database. The
+    Eigenfaces method already has a 97% recognition rate on it, so you won't see any great
+    improvements with other algorithms. The Yale Facedatabase A (also known as Yalefaces) is a more
+    appropriate dataset for initial experiments, because the recognition problem is harder. The
+    database consists of 15 people (14 male, 1 female) each with 11 grayscale images sized
+    \f$320 \times 243\f$ pixel. There are changes in the light conditions (center light, left light,
+    right light), facial expressions (happy, normal, sad, sleepy, surprised, wink) and glasses
+    (glasses, no-glasses).
+
+    The original images are not cropped and aligned. Please look into the @ref face_appendix for a
+    Python script, that does the job for you.
+
+-   [Extended Yale Facedatabase B](http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html) The
+    Extended Yale Facedatabase B contains 2414 images of 38 different people in its cropped version.
+    The focus of this database is set on extracting features that are robust to illumination, the
+    images have almost no variation in emotion/occlusion/... . I personally think, that this dataset
+    is too large for the experiments I perform in this document. You better use the [AT&T
+    Facedatabase](http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html) for intial
+    testing. A first version of the Yale Facedatabase B was used in @cite BHK97 to see how the
+    Eigenfaces and Fisherfaces method perform under heavy illumination changes. @cite Lee05 used the
+    same setup to take 16128 images of 28 people. The Extended Yale Facedatabase B is the merge of
+    the two databases, which is now known as Extended Yalefacedatabase B.
+
+### Preparing the data  {#tutorial_face_prepare}
+
+Once we have acquired some data, we'll need to read it in our program. In the demo applications I
+have decided to read the images from a very simple CSV file. Why? Because it's the simplest
+platform-independent approach I can think of. However, if you know a simpler solution please ping me
+about it. Basically all the CSV file needs to contain are lines composed of a filename followed by a
+; followed by the label (as *integer number*), making up a line like this:
+
+@code{.csv}
+/path/to/image.ext;0
+@endcode
+
+Let's dissect the line. /path/to/image.ext is the path to an image, probably something like this if
+you are in Windows: C:/faces/person0/image0.jpg. Then there is the separator ; and finally we assign
+the label 0 to the image. Think of the label as the subject (the person) this image belongs to, so
+same subjects (persons) should have the same label.
+
+Download the AT&T Facedatabase from AT&T Facedatabase and the corresponding CSV file from at.txt,
+which looks like this (file is without ... of course):
+
+@code{.csv}
+./at/s1/1.pgm;0
+./at/s1/2.pgm;0
+...
+./at/s2/1.pgm;1
+./at/s2/2.pgm;1
+...
+./at/s40/1.pgm;39
+./at/s40/2.pgm;39
+@endcode
+
+Imagine I have extracted the files to D:/data/at and have downloaded the CSV file to D:/data/at.txt.
+Then you would simply need to Search & Replace ./ with D:/data/. You can do that in an editor of
+your choice, every sufficiently advanced editor can do this. Once you have a CSV file with valid
+filenames and labels, you can run any of the demos by passing the path to the CSV file as parameter:
+
+@code{.sh}
+facerec_demo.exe D:/data/at.txt
+@endcode
+
+Please, see @ref tutorial_face_appendix_csv for details on creating CSV file.
+
+Eigenfaces  {#tutorial_face_eigenfaces}
+----------
+
+The problem with the image representation we are given is its high dimensionality. Two-dimensional
+\f$p \times q\f$ grayscale images span a \f$m = pq\f$-dimensional vector space, so an image with
+\f$100 \times 100\f$ pixels lies in a \f$10,000\f$-dimensional image space already. The question is: Are all
+dimensions equally useful for us? We can only make a decision if there's any variance in data, so
+what we are looking for are the components that account for most of the information. The Principal
+Component Analysis (PCA) was independently proposed by [Karl
+Pearson](http://en.wikipedia.org/wiki/Karl_Pearson) (1901) and [Harold
+Hotelling](http://en.wikipedia.org/wiki/Harold_Hotelling) (1933) to turn a set of possibly
+correlated variables into a smaller set of uncorrelated variables. The idea is, that a
+high-dimensional dataset is often described by correlated variables and therefore only a few
+meaningful dimensions account for most of the information. The PCA method finds the directions with
+the greatest variance in the data, called principal components.
+
+### Algorithmic Description of Eigenfaces method  {#tutorial_face_eigenfaces_algo}
+
+Let \f$X = \{ x_{1}, x_{2}, \ldots, x_{n} \}\f$ be a random vector with observations \f$x_i \in R^{d}\f$.
+
+1.  Compute the mean \f$\mu\f$
+
+\f[\mu = \frac{1}{n} \sum_{i=1}^{n} x_{i}\f]
+
+2.  Compute the the Covariance Matrix S
+
+\f[S = \frac{1}{n} \sum_{i=1}^{n} (x_{i} - \mu) (x_{i} - \mu)^{T}`\f]
+
+3.  Compute the eigenvalues \f$\lambda_{i}\f$ and eigenvectors \f$v_{i}\f$ of \f$S\f$
+
+\f[S v_{i} = \lambda_{i} v_{i}, i=1,2,\ldots,n\f]
+
+4.  Order the eigenvectors descending by their eigenvalue. The \f$k\f$ principal components are the
+    eigenvectors corresponding to the \f$k\f$ largest eigenvalues.
+
+The \f$k\f$ principal components of the observed vector \f$x\f$ are then given by:
+
+\f[y = W^{T} (x - \mu)\f]
+
+where \f$W = (v_{1}, v_{2}, \ldots, v_{k})\f$.
+
+The reconstruction from the PCA basis is given by:
+
+\f[x = W y + \mu\f]
+
+where \f$W = (v_{1}, v_{2}, \ldots, v_{k})\f$.
+
+The Eigenfaces method then performs face recognition by:
+
+-   Projecting all training samples into the PCA subspace.
+-   Projecting the query image into the PCA subspace.
+-   Finding the nearest neighbor between the projected training images and the projected query
+    image.
+
+Still there's one problem left to solve. Imagine we are given \f$400\f$ images sized \f$100 \times 100\f$
+pixel. The Principal Component Analysis solves the covariance matrix \f$S = X X^{T}\f$, where
+\f${size}(X) = 10000 \times 400\f$ in our example. You would end up with a \f$10000 \times 10000\f$ matrix,
+roughly \f$0.8 GB\f$. Solving this problem isn't feasible, so we'll need to apply a trick. From your
+linear algebra lessons you know that a \f$M \times N\f$ matrix with \f$M > N\f$ can only have \f$N - 1\f$
+non-zero eigenvalues. So it's possible to take the eigenvalue decomposition \f$S = X^{T} X\f$ of size
+\f$N \times N\f$ instead:
+
+\f[X^{T} X v_{i} = \lambda_{i} v{i}\f]
+
+and get the original eigenvectors of \f$S = X X^{T}\f$ with a left multiplication of the data matrix:
+
+\f[X X^{T} (X v_{i}) = \lambda_{i} (X v_{i})\f]
+
+The resulting eigenvectors are orthogonal, to get orthonormal eigenvectors they need to be
+normalized to unit length. I don't want to turn this into a publication, so please look into
+@cite Duda01 for the derivation and proof of the equations.
+
+### Eigenfaces in OpenCV  {#tutorial_face_eigenfaces_use}
+
+For the first source code example, I'll go through it with you. I am first giving you the whole
+source code listing, and after this we'll look at the most important lines in detail. Please note:
+every source code listing is commented in detail, so you should have no problems following it.
+
+The source code for this demo application is also available in the src folder coming with this
+documentation:
+
+@include face/samples/facerec_eigenfaces.cpp
+
+I've used the jet colormap, so you can see how the grayscale values are distributed within the
+specific Eigenfaces. You can see, that the Eigenfaces do not only encode facial features, but also
+the illumination in the images (see the left light in Eigenface \#4, right light in Eigenfaces \#5):
+
+![image](img/eigenfaces_opencv.png)
+
+We've already seen, that we can reconstruct a face from its lower dimensional approximation. So
+let's see how many Eigenfaces are needed for a good reconstruction. I'll do a subplot with
+\f$10,30,\ldots,310\f$ Eigenfaces:
+
+@code{.cpp}
+// Display or save the image reconstruction at some predefined steps:
+for(int num_components = 10; num_components < 300; num_components+=15) {
+    // slice the eigenvectors from the model
+    Mat evs = Mat(W, Range::all(), Range(0, num_components));
+    Mat projection = LDA::subspaceProject(evs, mean, images[0].reshape(1,1));
+    Mat reconstruction = LDA::subspaceReconstruct(evs, mean, projection);
+    // Normalize the result:
+    reconstruction = norm_0_255(reconstruction.reshape(1, images[0].rows));
+    // Display or save:
+    if(argc == 2) {
+        imshow(format("eigenface_reconstruction_%d", num_components), reconstruction);
+    } else {
+        imwrite(format("%s/eigenface_reconstruction_%d.png", output_folder.c_str(), num_components), reconstruction);
+    }
+}
+@endcode
+
+10 Eigenvectors are obviously not sufficient for a good image reconstruction, 50 Eigenvectors may
+already be sufficient to encode important facial features. You'll get a good reconstruction with
+approximately 300 Eigenvectors for the AT&T Facedatabase. There are rule of thumbs how many
+Eigenfaces you should choose for a successful face recognition, but it heavily depends on the input
+data. @cite Zhao03 is the perfect point to start researching for this:
+
+![image](img/eigenface_reconstruction_opencv.png)
+
+Fisherfaces  {#tutorial_face_fisherfaces}
+-----------
+
+The Principal Component Analysis (PCA), which is the core of the Eigenfaces method, finds a linear
+combination of features that maximizes the total variance in data. While this is clearly a powerful
+way to represent data, it doesn't consider any classes and so a lot of discriminative information
+*may* be lost when throwing components away. Imagine a situation where the variance in your data is
+generated by an external source, let it be the light. The components identified by a PCA do not
+necessarily contain any discriminative information at all, so the projected samples are smeared
+together and a classification becomes impossible (see
+[<http://www.bytefish.de/wiki/pca_lda_with_gnu_octave>](http://www.bytefish.de/wiki/pca_lda_with_gnu_octave)
+for an example).
+
+The Linear Discriminant Analysis performs a class-specific dimensionality reduction and was invented
+by the great statistician [Sir R. A. Fisher](http://en.wikipedia.org/wiki/Ronald_Fisher). He
+successfully used it for classifying flowers in his 1936 paper *The use of multiple measurements in
+taxonomic problems* @cite Fisher36 . In order to find the combination of features that separates best
+between classes the Linear Discriminant Analysis maximizes the ratio of between-classes to
+within-classes scatter, instead of maximizing the overall scatter. The idea is simple: same classes
+should cluster tightly together, while different classes are as far away as possible from each other
+in the lower-dimensional representation. This was also recognized by
+[Belhumeur](http://www.cs.columbia.edu/~belhumeur/), [Hespanha](http://www.ece.ucsb.edu/~hespanha/)
+and [Kriegman](http://cseweb.ucsd.edu/~kriegman/) and so they applied a Discriminant Analysis to
+face recognition in @cite BHK97 .
+
+### Algorithmic Description of Fisherfaces method {#tutorial_face_fisherfaces_algo}
+
+Let \f$X\f$ be a random vector with samples drawn from \f$c\f$ classes:
+
+\f[\begin{align*}
+    X & = & \{X_1,X_2,\ldots,X_c\} \\
+    X_i & = & \{x_1, x_2, \ldots, x_n\}
+\end{align*}\f]
+
+The scatter matrices \f$S_{B}\f$ and S\_{W} are calculated as:
+
+\f[\begin{align*}
+    S_{B} & = & \sum_{i=1}^{c} N_{i} (\mu_i - \mu)(\mu_i - \mu)^{T} \\
+    S_{W} & = & \sum_{i=1}^{c} \sum_{x_{j} \in X_{i}} (x_j - \mu_i)(x_j - \mu_i)^{T}
+\end{align*}\f]
+
+, where \f$\mu\f$ is the total mean:
+
+\f[\mu = \frac{1}{N} \sum_{i=1}^{N} x_i\f]
+
+And \f$\mu_i\f$ is the mean of class \f$i \in \{1,\ldots,c\}\f$:
+
+\f[\mu_i = \frac{1}{|X_i|} \sum_{x_j \in X_i} x_j\f]
+
+Fisher's classic algorithm now looks for a projection \f$W\f$, that maximizes the class separability
+criterion:
+
+\f[W_{opt} = \operatorname{arg\,max}_{W} \frac{|W^T S_B W|}{|W^T S_W W|}\f]
+
+Following @cite BHK97, a solution for this optimization problem is given by solving the General
+Eigenvalue Problem:
+
+\f[\begin{align*}
+    S_{B} v_{i} & = & \lambda_{i} S_w v_{i} \nonumber \\
+    S_{W}^{-1} S_{B} v_{i} & = & \lambda_{i} v_{i}
+\end{align*}\f]
+
+There's one problem left to solve: The rank of \f$S_{W}\f$ is at most \f$(N-c)\f$, with \f$N\f$ samples and \f$c\f$
+classes. In pattern recognition problems the number of samples \f$N\f$ is almost always samller than the
+dimension of the input data (the number of pixels), so the scatter matrix \f$S_{W}\f$ becomes singular
+(see @cite RJ91). In @cite BHK97 this was solved by performing a Principal Component Analysis on the
+data and projecting the samples into the \f$(N-c)\f$-dimensional space. A Linear Discriminant Analysis
+was then performed on the reduced data, because \f$S_{W}\f$ isn't singular anymore.
+
+The optimization problem can then be rewritten as:
+
+\f[\begin{align*}
+    W_{pca} & = & \operatorname{arg\,max}_{W} |W^T S_T W| \\
+    W_{fld} & = & \operatorname{arg\,max}_{W} \frac{|W^T W_{pca}^T S_{B} W_{pca} W|}{|W^T W_{pca}^T S_{W} W_{pca} W|}
+\end{align*}\f]
+
+The transformation matrix \f$W\f$, that projects a sample into the \f$(c-1)\f$-dimensional space is then
+given by:
+
+\f[W = W_{fld}^{T} W_{pca}^{T}\f]
+
+### Fisherfaces in OpenCV {#tutorial_face_fisherfaces_use}
+
+The source code for this demo application is also available in the src folder coming with this
+documentation:
+
+@include face/samples/facerec_fisherfaces.cpp
+
+For this example I am going to use the Yale Facedatabase A, just because the plots are nicer. Each
+Fisherface has the same length as an original image, thus it can be displayed as an image. The demo
+shows (or saves) the first, at most 16 Fisherfaces:
+
+![image](img/fisherfaces_opencv.png)
+
+The Fisherfaces method learns a class-specific transformation matrix, so the they do not capture
+illumination as obviously as the Eigenfaces method. The Discriminant Analysis instead finds the
+facial features to discriminate between the persons. It's important to mention, that the performance
+of the Fisherfaces heavily depends on the input data as well. Practically said: if you learn the
+Fisherfaces for well-illuminated pictures only and you try to recognize faces in bad-illuminated
+scenes, then method is likely to find the wrong components (just because those features may not be
+predominant on bad illuminated images). This is somewhat logical, since the method had no chance to
+learn the illumination.
+
+The Fisherfaces allow a reconstruction of the projected image, just like the Eigenfaces did. But
+since we only identified the features to distinguish between subjects, you can't expect a nice
+reconstruction of the original image. For the Fisherfaces method we'll project the sample image onto
+each of the Fisherfaces instead. So you'll have a nice visualization, which feature each of the
+Fisherfaces describes:
+
+@code{.cpp}
+// Display or save the image reconstruction at some predefined steps:
+for(int num_component = 0; num_component < min(16, W.cols); num_component++) {
+    // Slice the Fisherface from the model:
+    Mat ev = W.col(num_component);
+    Mat projection = LDA::subspaceProject(ev, mean, images[0].reshape(1,1));
+    Mat reconstruction = LDA::subspaceReconstruct(ev, mean, projection);
+    // Normalize the result:
+    reconstruction = norm_0_255(reconstruction.reshape(1, images[0].rows));
+    // Display or save:
+    if(argc == 2) {
+        imshow(format("fisherface_reconstruction_%d", num_component), reconstruction);
+    } else {
+        imwrite(format("%s/fisherface_reconstruction_%d.png", output_folder.c_str(), num_component), reconstruction);
+    }
+}
+@endcode
+
+The differences may be subtle for the human eyes, but you should be able to see some differences:
+
+![image](img/fisherface_reconstruction_opencv.png)
+
+Local Binary Patterns Histograms {#tutorial_face_lbph}
+--------------------------------
+
+Eigenfaces and Fisherfaces take a somewhat holistic approach to face recognition. You treat your
+data as a vector somewhere in a high-dimensional image space. We all know high-dimensionality is
+bad, so a lower-dimensional subspace is identified, where (probably) useful information is
+preserved. The Eigenfaces approach maximizes the total scatter, which can lead to problems if the
+variance is generated by an external source, because components with a maximum variance over all
+classes aren't necessarily useful for classification (see
+[<http://www.bytefish.de/wiki/pca_lda_with_gnu_octave>](http://www.bytefish.de/wiki/pca_lda_with_gnu_octave)).
+So to preserve some discriminative information we applied a Linear Discriminant Analysis and
+optimized as described in the Fisherfaces method. The Fisherfaces method worked great... at least
+for the constrained scenario we've assumed in our model.
+
+Now real life isn't perfect. You simply can't guarantee perfect light settings in your images or 10
+different images of a person. So what if there's only one image for each person? Our covariance
+estimates for the subspace *may* be horribly wrong, so will the recognition. Remember the Eigenfaces
+method had a 96% recognition rate on the AT&T Facedatabase? How many images do we actually need to
+get such useful estimates? Here are the Rank-1 recognition rates of the Eigenfaces and Fisherfaces
+method on the AT&T Facedatabase, which is a fairly easy image database:
+
+![image](img/at_database_small_sample_size.png)
+
+So in order to get good recognition rates you'll need at least 8(+-1) images for each person and the
+Fisherfaces method doesn't really help here. The above experiment is a 10-fold cross validated
+result carried out with the facerec framework at:
+[<https://github.com/bytefish/facerec>](https://github.com/bytefish/facerec). This is not a
+publication, so I won't back these figures with a deep mathematical analysis. Please have a look
+into @cite KM01 for a detailed analysis of both methods, when it comes to small training datasets.
+
+So some research concentrated on extracting local features from images. The idea is to not look at
+the whole image as a high-dimensional vector, but describe only local features of an object. The
+features you extract this way will have a low-dimensionality implicitly. A fine idea! But you'll
+soon observe the image representation we are given doesn't only suffer from illumination variations.
+Think of things like scale, translation or rotation in images - your local description has to be at
+least a bit robust against those things. Just like SIFT, the Local Binary Patterns methodology has
+its roots in 2D texture analysis. The basic idea of Local Binary Patterns is to summarize the local
+structure in an image by comparing each pixel with its neighborhood. Take a pixel as center and
+threshold its neighbors against. If the intensity of the center pixel is greater-equal its neighbor,
+then denote it with 1 and 0 if not. You'll end up with a binary number for each pixel, just like
+11001111. So with 8 surrounding pixels you'll end up with 2\^8 possible combinations, called *Local
+Binary Patterns* or sometimes referred to as *LBP codes*. The first LBP operator described in
+literature actually used a fixed 3 x 3 neighborhood just like this:
+
+![image](img/lbp/lbp.png)
+
+### Algorithmic Description of LBPH method {#tutorial_face_lbph_algo}
+
+A more formal description of the LBP operator can be given as:
+
+\f[LBP(x_c, y_c) = \sum_{p=0}^{P-1} 2^p s(i_p - i_c)\f]
+
+, with \f$(x_c, y_c)\f$ as central pixel with intensity \f$i_c\f$; and \f$i_n\f$ being the intensity of the the
+neighbor pixel. \f$s\f$ is the sign function defined as:
+
+\f[\begin{equation}
+s(x) =
+\begin{cases}
+1 & \text{if \(x \geq 0\)}\\
+0 & \text{else}
+\end{cases}
+\end{equation}\f]
+
+This description enables you to capture very fine grained details in images. In fact the authors
+were able to compete with state of the art results for texture classification. Soon after the
+operator was published it was noted, that a fixed neighborhood fails to encode details differing in
+scale. So the operator was extended to use a variable neighborhood in @cite AHP04 . The idea is to
+align an abritrary number of neighbors on a circle with a variable radius, which enables to capture
+the following neighborhoods:
+
+![image](img/lbp/patterns.png)
+
+For a given Point \f$(x_c,y_c)\f$ the position of the neighbor \f$(x_p,y_p), p \in P\f$ can be calculated
+by:
+
+\f[\begin{align*}
+x_{p} & = & x_c + R \cos({\frac{2\pi p}{P}})\\
+y_{p} & = & y_c - R \sin({\frac{2\pi p}{P}})
+\end{align*}\f]
+
+Where \f$R\f$ is the radius of the circle and \f$P\f$ is the number of sample points.
+
+The operator is an extension to the original LBP codes, so it's sometimes called *Extended LBP*
+(also referred to as *Circular LBP*) . If a points coordinate on the circle doesn't correspond to
+image coordinates, the point get's interpolated. Computer science has a bunch of clever
+interpolation schemes, the OpenCV implementation does a bilinear interpolation:
+
+\f[\begin{align*}
+f(x,y) \approx \begin{bmatrix}
+    1-x & x \end{bmatrix} \begin{bmatrix}
+    f(0,0) & f(0,1) \\
+    f(1,0) & f(1,1) \end{bmatrix} \begin{bmatrix}
+    1-y \\
+    y \end{bmatrix}.
+\end{align*}\f]
+
+By definition the LBP operator is robust against monotonic gray scale transformations. We can easily
+verify this by looking at the LBP image of an artificially modified image (so you see what an LBP
+image looks like!):
+
+![image](img/lbp/lbp_yale.jpg)
+
+So what's left to do is how to incorporate the spatial information in the face recognition model.
+The representation proposed by Ahonen et. al @cite AHP04 is to divide the LBP image into \f$m\f$ local
+regions and extract a histogram from each. The spatially enhanced feature vector is then obtained by
+concatenating the local histograms (**not merging them**). These histograms are called *Local Binary
+Patterns Histograms*.
+
+### Local Binary Patterns Histograms in OpenCV {#tutorial_face_lbph_use}
+
+The source code for this demo application is also available in the src folder coming with this
+documentation:
+
+@include face/samples/facerec_lbph.cpp
+
+Conclusion {#tutorial_face_conclusion}
+----------
+
+You've learned how to use the new FaceRecognizer in real applications. After reading the document
+you also know how the algorithms work, so now it's time for you to experiment with the available
+algorithms. Use them, improve them and let the OpenCV community participate!
+
+Credits {#tutorial_face_credits}
+-------
+
+This document wouldn't be possible without the kind permission to use the face images of the *AT&T
+Database of Faces* and the *Yale Facedatabase A/B*.
+
+### The Database of Faces {#tutorial_face_credits_db}
+
+__Important: when using these images, please give credit to "AT&T Laboratories, Cambridge."__
+
+The Database of Faces, formerly *The ORL Database of Faces*, contains a set of face images taken
+between April 1992 and April 1994. The database was used in the context of a face recognition
+project carried out in collaboration with the Speech, Vision and Robotics Group of the Cambridge
+University Engineering Department.
+
+There are ten different images of each of 40 distinct subjects. For some subjects, the images were
+taken at different times, varying the lighting, facial expressions (open / closed eyes, smiling /
+not smiling) and facial details (glasses / no glasses). All the images were taken against a dark
+homogeneous background with the subjects in an upright, frontal position (with tolerance for some
+side movement).
+
+The files are in PGM format. The size of each image is 92x112 pixels, with 256 grey levels per
+pixel. The images are organised in 40 directories (one for each subject), which have names of the
+form sX, where X indicates the subject number (between 1 and 40). In each of these directories,
+there are ten different images of that subject, which have names of the form Y.pgm, where Y is the
+image number for that subject (between 1 and 10).
+
+A copy of the database can be retrieved from:
+[<http://www.cl.cam.ac.uk/research/dtg/attarchive/pub/data/att_faces.zip>](http://www.cl.cam.ac.uk/research/dtg/attarchive/pub/data/att_faces.zip).
+
+### Yale Facedatabase A {#tutorial_face_credits_yalea}
+
+*With the permission of the authors I am allowed to show a small number of images (say subject 1 and
+all the variations) and all images such as Fisherfaces and Eigenfaces from either Yale Facedatabase
+A or the Yale Facedatabase B.*
+
+The Yale Face Database A (size 6.4MB) contains 165 grayscale images in GIF format of 15 individuals.
+There are 11 images per subject, one per different facial expression or configuration: center-light,
+w/glasses, happy, left-light, w/no glasses, normal, right-light, sad, sleepy, surprised, and wink.
+(Source:
+[<http://cvc.yale.edu/projects/yalefaces/yalefaces.html>](http://cvc.yale.edu/projects/yalefaces/yalefaces.html))
+
+### Yale Facedatabase B {#tutorial_face_credits_yaleb}
+
+*With the permission of the authors I am allowed to show a small number of images (say subject 1 and
+all the variations) and all images such as Fisherfaces and Eigenfaces from either Yale Facedatabase
+A or the Yale Facedatabase B.*
+
+The extended Yale Face Database B contains 16128 images of 28 human subjects under 9 poses and 64
+illumination conditions. The data format of this database is the same as the Yale Face Database B.
+Please refer to the homepage of the Yale Face Database B (or one copy of this page) for more
+detailed information of the data format.
+
+You are free to use the extended Yale Face Database B for research purposes. All publications which
+use this database should acknowledge the use of "the Exteded Yale Face Database B" and reference
+Athinodoros Georghiades, Peter Belhumeur, and David Kriegman's paper, "From Few to Many:
+Illumination Cone Models for Face Recognition under Variable Lighting and Pose", PAMI, 2001,
+[[bibtex]](http://vision.ucsd.edu/~leekc/ExtYaleDatabase/athosref.html).
+
+The extended database as opposed to the original Yale Face Database B with 10 subjects was first
+reported by Kuang-Chih Lee, Jeffrey Ho, and David Kriegman in "Acquiring Linear Subspaces for Face
+Recognition under Variable Lighting, PAMI, May, 2005
+[[pdf]](http://vision.ucsd.edu/~leekc/papers/9pltsIEEE.pdf)." All test image data used in the
+experiments are manually aligned, cropped, and then re-sized to 168x192 images. If you publish your
+experimental results with the cropped images, please reference the PAMI2005 paper as well. (Source:
+[<http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html>](http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html))
+
+Appendix {#face_appendix}
+--------
+
+### Creating the CSV File {#tutorial_face_appendix_csv}
+
+You don't really want to create the CSV file by hand. I have prepared you a little Python script
+`create_csv.py` (you find it at `src/create_csv.py` coming with this tutorial) that automatically
+creates you a CSV file. If you have your images in hierarchie like this
+(`/basepath/<subject>/<image.ext>`):
+
+@code{.sh}
+philipp@mango:~/facerec/data/at$ tree
+.
+|-- s1
+|   |-- 1.pgm
+|   |-- ...
+|   |-- 10.pgm
+|-- s2
+|   |-- 1.pgm
+|   |-- ...
+|   |-- 10.pgm
+...
+|-- s40
+|   |-- 1.pgm
+|   |-- ...
+|   |-- 10.pgm
+@endcode
+
+Then simply call `create_csv.py at` , here 'at' being the basepath to the folder, just like this and you could save the
+output:
+
+@code{.sh}
+philipp@mango:~/facerec/data$ python create_csv.py at
+at/s13/2.pgm;0
+at/s13/7.pgm;0
+at/s13/6.pgm;0
+at/s13/9.pgm;0
+at/s13/5.pgm;0
+at/s13/3.pgm;0
+at/s13/4.pgm;0
+at/s13/10.pgm;0
+at/s13/8.pgm;0
+at/s13/1.pgm;0
+at/s17/2.pgm;1
+at/s17/7.pgm;1
+at/s17/6.pgm;1
+at/s17/9.pgm;1
+at/s17/5.pgm;1
+at/s17/3.pgm;1
+[...]
+@endcode
+
+Here is the script, if you can't find it:
+
+@verbinclude face/samples/etc/create_csv.py
+
+### Aligning Face Images {#tutorial_face_appendix_align}
+
+An accurate alignment of your image data is especially important in tasks like emotion detection,
+were you need as much detail as possible. Believe me... You don't want to do this by hand. So I've
+prepared you a tiny Python script. The code is really easy to use. To scale, rotate and crop the
+face image you just need to call *CropFace(image, eye_left, eye_right, offset_pct, dest_sz)*,
+where:
+
+-   *eye_left* is the position of the left eye
+-   *eye_right* is the position of the right eye
+-   *offset_pct* is the percent of the image you want to keep next to the eyes (horizontal,
+    vertical direction)
+-   *dest_sz* is the size of the output image
+
+If you are using the same *offset_pct* and *dest_sz* for your images, they are all aligned at the
+eyes.
+
+@verbinclude face/samples/etc/crop_face.py
+
+Imagine we are given [this photo of Arnold
+Schwarzenegger](http://en.wikipedia.org/wiki/File:Arnold_Schwarzenegger_edit%28ws%29.jpg), which is
+under a Public Domain license. The (x,y)-position of the eyes is approximately *(252,364)* for the
+left and *(420,366)* for the right eye. Now you only need to define the horizontal offset, vertical
+offset and the size your scaled, rotated & cropped face should have.
+
+Here are some examples:
+
+Configuration                   | Cropped, Scaled, Rotated Face
+--------------------------------|------------------------------------------------------------------
+0.1 (10%), 0.1 (10%), (200,200) | ![](tutorials/gender_classification/arnie_10_10_200_200.jpg)
+0.2 (20%), 0.2 (20%), (200,200) | ![](tutorials/gender_classification/arnie_20_20_200_200.jpg)
+0.3 (30%), 0.3 (30%), (200,200) | ![](tutorials/gender_classification/arnie_30_30_200_200.jpg)
+0.2 (20%), 0.2 (20%), (70,70)   | ![](tutorials/gender_classification/arnie_20_20_70_70.jpg)
+
+### CSV for the AT&T Facedatabase {#tutorial_face_appendix_attcsv}
+
+@verbinclude face/samples/etc/at.txt
@@ -0,0 +1,44 @@
+Tutorials for face module {#tutorial_table_of_content_face}
+=========================
+
+-   @subpage tutorial_face_main
+
+    Face Recognition using OpenCV
+
+-   @subpage tutorial_face_landmark_detection_in_an_image
+
+    *Compatibility:* \> OpenCV 3.3
+
+    *Author:* Sukhad Anand
+
+    *Mentor:* Steven Puttemans
+
+    Face landmark detection in an image using ensemble of regression trees
+
+-   @subpage tutorial_face_training_face_landmark_detector
+
+    *Compatibility:* \> OpenCV 3.3
+
+    *Author:* Sukhad Anand
+
+    *Mentor:* Steven Puttemans
+
+    Training a face landmark detector using an ensemble of regression trees
+
+-   @subpage tutorial_face_landmark_detection_in_video
+
+    *Compatibility:* \> OpenCV 3.3
+
+    *Author:* Sukhad Anand
+
+    *Mentor:* Steven Puttemans
+
+    Face lanmark detection in a video running at real time
+
+-   @subpage tutorial_face_swapping_face_landmark_detection
+
+    *Author:* Sukhad Anand
+
+    *Mentor:* Steven Puttemans
+
+    Basic application to swap faces using face landmark detection
@@ -0,0 +1,113 @@
+Using the FacemarkAAM {#tutorial_facemark_aam}
+==========================================================
+
+Goals
+----
+
+In this tutorial you will learn how to:
+- creating the instance of FacemarkAAM
+- training the AAM model
+- Fitting using FacemarkAAM
+
+Preparation
+--------
+
+Before you continue with this tutorial, you should download the dataset of facial landmarks detection.
+We suggest you to download the LFPW dataset which can be retrieved at <https://ibug.doc.ic.ac.uk/download/annotations/lfpw.zip>.
+
+Make sure that the annotation format is supported by the API, the contents in annotation file should look like the following snippet:
+@code
+version: 1
+n_points:  68
+{
+212.716603 499.771793
+230.232816 566.290071
+...
+}
+@endcode
+
+The next thing to do is to make 2 text files containing the list of image files and annotation files respectively. Make sure that the order or image and annotation in both files are matched. Furthermore, it is advised to use absolute path instead of relative path.
+Example to make the file list in Linux machine
+@code
+ls $PWD/trainset/*.jpg > images_train.txt
+ls $PWD/trainset/*.pts > annotation_train.txt
+@endcode
+
+example of content in the images_train.txt
+@code
+/home/user/lfpw/trainset/100032540_1.jpg
+/home/user/lfpw/trainset/100040721_1.jpg
+/home/user/lfpw/trainset/100040721_2.jpg
+/home/user/lfpw/trainset/1002681492_1.jpg
+@endcode
+
+example of content in the annotation_train.txt
+@code
+/home/user/lfpw/trainset/100032540_1.pts
+/home/user/lfpw/trainset/100040721_1.pts
+/home/user/lfpw/trainset/100040721_2.pts
+/home/user/lfpw/trainset/1002681492_1.pts
+@endcode
+
+Optionally, you can create the similar files for the testset.
+
+In this tutorial, the pre-trained model will not be provided due to its large file size (~500MB). By following this tutorial, you will be able to train obtain your own trained model within few minutes.
+
+Working with the AAM Algorithm
+--------
+
+The full working code is available in the face/samples/facemark_demo_aam.cpp file. In this tutorial, the explanation of some important parts are covered.
+
+-# <B>Creating the instance of AAM algorithm</B>
+
+   @snippet face/samples/facemark_demo_aam.cpp instance_creation
+   Firstly, an instance of parameter for the AAM algorithm is created. In this case, we will modify the default list of the scaling factor. By default, the scaling factor used is 1.0 (no scaling). Here we add two more scaling factor which will make the instance trains two more model at scale 2 and 4 (2 time smaller and 4 time smaller, faster faster fitting time). However, you should make sure that this scaling factor is not too big since it will make the image scaled into a very small one. Thus it will lost all of its important information for the landmark detection purpose.
+
+   Alternatively, you can override the default scaling in similar way to this example:
+   @code
+   std::vector<float>scales;
+   scales.push_back(1.5);
+   scales.push_back(2.4);
+
+   FacemarkAAM::Params params;
+   params.scales = scales;
+   @endcode
+
+-# <B>Loading the dataset</B>
+
+   @snippet face/samples/facemark_demo_aam.cpp load_dataset
+   List of the dataset are loaded into the program. We will put the samples from dataset one by one in the next step.
+
+-# <B>Adding the samples to the trainer</B>
+
+   @snippet face/samples/facemark_demo_aam.cpp add_samples
+   The image from the dataset list are loaded one by one as well as its corresponding annotation data. Then the pair of sample is added to the trainer.
+
+-# <B>Training process</B>
+
+   @snippet face/samples/facemark_demo_aam.cpp training
+   The training process is called using a single line of code. Make sure that all the required training samples are already added to the trainer.
+
+-# <B>Preparation for fitting</B>
+
+   First of all, you need to load the list of test files.
+   @snippet face/samples/facemark_demo_aam.cpp load_test_images
+
+   Since the AAM needs initialization parameters (rotation, translation, and scaling), you need to declare the required variable to store these information which will be obtained using a custom function. Since the implementation of getInitialFitting() function in this example is not optimal, you can create your own function.
+
+   The initialization is obtained by comparing the base shape of the trained model with the current face image. In this case, the rotation is obtained by comparing the angle of line formed by two eyes in the input face image with the same line in the base shape. Meanwhile, the scaling is obtained by comparing the length of line between eyes in the input image compared to the base shape.
+
+-# <B>Fitting process</B>
+
+   The fitting process is started by detecting the face in a given image.
+   @snippet face/samples/facemark_demo_aam.cpp detect_face
+
+   If at least one face is found, then the next step is computing the initialization parameters. In this case, since the getInitialFitting() function is not optimal, it may not find pair of eyes from a given face. Therefore, we will filter out the face without initialization parameters and in this case, each element in the `conf` vector represent the initialization parameter for each filtered face.
+   @snippet face/samples/facemark_demo_aam.cpp get_initialization
+
+   For the fitting parameter stored in the `conf` vector, the last parameter represent the ID of scaling factor that will be used in the fitting process. In this example the fitting will use the biggest scaling factor (4) which is expected to have the fastest computation time compared to the other scales. If the ID if bigger than the available trained scale in the model, the the model with the biggest scale ID is used.
+
+   The fitting process is quite simple, you just need to put the corresponding image, vector of `cv::Rect` representing the ROIs of all faces in the given image, container of the landmark points represented by `landmarks` variable, and the configuration variables.
+   @snippet face/samples/facemark_demo_aam.cpp fitting_process
+
+   After the fitting process is finished, you can visualize the result using the `drawFacemarks` function.
@@ -0,0 +1,273 @@
+Adding a new algorithm to the Facemark API {#tutorial_facemark_add_algorithm}
+==========================================================
+
+Goals
+----
+
+In this tutorial you will learn how to:
+- integrate a new algorithm of facial landmark detector into the Facemark API
+- compile a specific contrib module
+- using extra parameters in a function
+
+
+Explanation
+-----------
+
+-  **Add the class header**
+
+    The class header for a new algorithm should be added to a new file in include/opencv2/face.
+    Here is the template that you can use to integrate a new algorithm, change the FacemarkNEW to a representative name of the new algorithm and save it using a representative filename accordingly.
+
+        @code{.cpp}
+        class CV_EXPORTS_W FacemarkNEW : public Facemark {
+        public:
+            struct CV_EXPORTS Config {
+                Config();
+
+                /*read only parameters - just for example*/
+                double detect_thresh;         //!<  detection confidence threshold
+                double sigma;                 //!<  another parameter
+
+                void read(const FileNode& /*fn*/);
+                void write(FileStorage& /*fs*/) const;
+            };
+
+            /*Builder and destructor*/
+            static Ptr<FacemarkNEW> create(const FacemarkNEW::Config &conf = FacemarkNEW::Config() );
+            virtual ~FacemarkNEW(){};
+        };
+        @endcode
+
+
+-  **Add the implementation code**
+
+    Create a new file in the source folder with name representing the new algorithm.
+    Here is the template that you can use.
+
+        @code{.cpp}
+        #include "opencv2/face.hpp"
+        #include "precomp.hpp"
+
+        namespace cv
+        {
+            FacemarkNEW::Config::Config(){
+                detect_thresh = 0.5;
+                sigma=0.2;
+            }
+
+            void FacemarkNEW::Config::read( const cv::FileNode& fn ){
+                *this = FacemarkNEW::Config();
+
+                if (!fn["detect_thresh"].empty())
+                    fn["detect_thresh"] >> detect_thresh;
+
+                if (!fn["sigma"].empty())
+                    fn["sigma"] >> sigma;
+
+            }
+
+            void FacemarkNEW::Config::write( cv::FileStorage& fs ) const{
+                fs << "detect_thresh" << detect_thresh;
+                fs << "sigma" << sigma;
+            }
+
+            /*implementation of the algorithm is in this class*/
+            class FacemarkNEWImpl : public FacemarkNEW {
+            public:
+                FacemarkNEWImpl( const FacemarkNEW::Config &conf = FacemarkNEW::Config() );
+
+                void read( const FileNode& /*fn*/ );
+                void write( FileStorage& /*fs*/ ) const;
+
+                void loadModel(String filename);
+
+                bool setFaceDetector(bool(*f)(InputArray , OutputArray, void * extra_params));
+                bool getFaces( InputArray image , OutputArray faces, void * extra_params);
+
+                Config config;
+
+            protected:
+
+                bool addTrainingSample(InputArray image, InputArray landmarks);
+                void training();
+                bool fit(InputArray image, InputArray faces, InputOutputArray landmarks, void * runtime_params);
+
+                Config config; // configurations
+
+                /*proxy to the user defined face detector function*/
+                bool(*faceDetector)(InputArray , OutputArray, void * );
+            }; // class
+
+            Ptr<FacemarkNEW> FacemarkNEW::create(const FacemarkNEW::Config &conf){
+                return Ptr<FacemarkNEWImpl>(new FacemarkNEWImpl(conf));
+            }
+
+            FacemarkNEWImpl::FacemarkNEWImpl( const FacemarkNEW::Config &conf ) :
+                config( conf )
+            {
+                // other initialization
+            }
+
+            bool FacemarkNEWImpl::addTrainingSample(InputArray image, InputArray landmarks){
+                // pre-process and save the new training sample
+                return true;
+            }
+
+            void FacemarkNEWImpl::training(){
+                printf("training\n");
+            }
+
+            bool FacemarkNEWImpl::fit(
+                InputArray image,
+                InputArray faces,
+                InputOutputArray landmarks,
+                void * runtime_params)
+            {
+                if(runtime_params!=0){
+                    // do something based on the extra parameters
+                }
+
+                printf("fitting\n");
+                return 0;
+            }
+
+            void FacemarkNEWImpl::read( const cv::FileNode& fn ){
+                config.read( fn );
+            }
+
+            void FacemarkNEWImpl::write( cv::FileStorage& fs ) const {
+                config.write( fs );
+            }
+
+            void FacemarkNEWImpl::loadModel(String filename){
+                // load the model
+            }
+
+            bool FacemarkNEWImpl::setFaceDetector(bool(*f)(InputArray , OutputArray, void * extra_params )){
+                faceDetector = f;
+                isSetDetector = true;
+                return true;
+            }
+
+            bool FacemarkNEWImpl::getFaces( InputArray image , OutputArray roi, void * extra_params){
+                if(!isSetDetector){
+                    return false;
+                }
+
+                if(extra_params!=0){
+                    //extract the extra parameters
+                }
+
+                std::vector<Rect> & faces = *(std::vector<Rect>*)roi.getObj();
+                faces.clear();
+
+                faceDetector(image.getMat(), faces, extra_params);
+
+                return true;
+            }
+        }
+
+        @endcode
+
+-  **Compiling the code**
+
+    Clear the build folder and then rebuild the entire library.
+    Note that you can deactivate the compilation of other contrib modules by adding "-D BUILD_opencv_<MODULE_NAME>=OFF" flag to the cmake.
+    After that you can execute make command in "<build_folder>/modules/face" to speed up the compiling process.
+
+Best Practice
+-----------
+- **Handling the extra parameters**
+    To handle the extra parameters, a new struct should be created to holds all the required parameters.
+    Here is an example of of a parameters container
+    @code
+    struct CV_EXPORTS Params
+    {
+        Params( Mat rot = Mat::eye(2,2,CV_32F),
+                Point2f trans = Point2f(0.0,0.0),
+                float scaling = 1.0
+        );
+
+        Mat R;
+        Point2f t;
+        float scale;
+    };
+    @endcode
+
+    Here is a snippet to extract the extra parameters:
+    @code
+    if(runtime_params!=0){
+        Telo*  conf = (Telo*)params;
+        Params* params
+        std::vector<Params> params = *(std::vector<Params>*)runtime_params;
+        for(size_t i=0; i<params.size();i++){
+            fit(img, landmarks[i], params[i].R,params[i].t, params[i].scale);
+        }
+    }else{
+        // do something
+    }
+    @endcode
+
+    And here is an example to pass the extra parameter into fit function
+    @code
+    FacemarkAAM::Params * params = new FacemarkAAM::Params(R,T,scale);
+    facemark->fit(image, faces, landmarks, params)
+    @endcode
+
+    In order to understand this scheme, here is a simple example that you can try to compile and see how it works.
+    @code
+    struct Params{
+        int x,y;
+        Params(int _x, int _y);
+    };
+    Params::Params(int _x,int _y){
+        x = _x;
+        y = _y;
+    }
+
+    void test(int a, void * params=0){
+        printf("a:%i\n", a);
+        if(params!=0){
+            Params*  params = (Params*)params;
+            printf("extra parameters:%i %i\n", params->x, params->y);
+        }
+    }
+
+    int main(){
+        Params* params = new Params(7,22);
+        test(99, params);
+        return 0;
+    }
+    @endcode
+
+- **Minimize the dependency**
+    It is highly recomended to keep the code as small as possible when compiled. For this purpose, the developers are ecouraged to avoid the needs of heavy dependency such as `imgcodecs` and `highgui`.
+
+- **Documentation and examples**
+    Please update the documentation whenever needed and put example code for the new algorithm.
+
+- **Test codes**
+    An algorithm should be accompanied with its corresponding test code to ensure that the algorithm is compatible with various types of environment (Linux, Windows64, Windows32, Android, etc). There are several basic test that should be performed as demonstrated in the test/test_facemark_lbf.cpp file including cration of its instance, add training data, perform the training process, load a trained model, and perform the fitting to obtain facial landmarks.
+
+- **Data organization**
+    It is advised to divide the data for a new algorithm into 3 parts :
+    @code
+    class CV_EXPORTS_W FacemarkNEW : public Facemark {
+    public:
+        struct CV_EXPORTS Params
+        {
+            // variables utilized as extra parameters
+        }
+        struct CV_EXPORTS Config
+        {
+            // variables used to configure the algorithm
+        }
+        struct CV_EXPORTS Model
+        {
+            // variables to store the information of model
+        }
+
+        static Ptr<FacemarkNEW> create(const FacemarkNEW::Config &conf = FacemarkNEW::Config() );
+        virtual ~FacemarkNEW(){};
+    }
+    @endcode
@@ -0,0 +1,29 @@
+Tutorial on Facial Landmark Detector API {#tutorial_table_of_content_facemark}
+==========================================================
+
+The facial landmark detector API is useful to detect facial landmarks from an input image.
+
+-   @subpage tutorial_facemark_add_algorithm
+
+    *Compatibility:* \> OpenCV 3.0
+
+    *Author:* Laksono Kurnianggoro
+
+    Adding a new algorithm in to the API.
+
+
+-   @subpage tutorial_facemark_usage
+
+    *Compatibility:* \> OpenCV 3.0
+
+    *Author:* Laksono Kurnianggoro
+
+    Tutorial on how to use the API.
+
+-   @subpage tutorial_facemark_aam
+
+    *Compatibility:* \> OpenCV 3.0
+
+    *Author:* Laksono Kurnianggoro
+
+    Tutorial on how to use the FacemarkAAM algorithm.
@@ -0,0 +1,176 @@
+Using the Facemark API {#tutorial_facemark_usage}
+==========================================================
+Goals
+----
+
+In this tutorial will helps you to
+
+-   Create a Facemark object.
+-   Set a user defined face detector for the facemark algorithm
+-   Train the algorithm.
+-   Use the trained model to detect the facial landmarks from a given image.
+
+Preparation
+---------
+
+Before you continue with this tutorial, you should download the dataset of facial landmarks detection.
+We suggest you to download the helen dataset which can be retrieved at <http://www.ifp.illinois.edu/~vuongle2/helen/>  (Caution! The algorithm requires around 9GB of RAM to train on this dataset).
+
+Make sure that the annotation format is supported by the API, the contents in annotation file should look like the following snippet:
+@code
+version: 1
+n_points:  68
+{
+212.716603 499.771793
+230.232816 566.290071
+...
+}
+@endcode
+
+The next thing to do is to make 2 text files containing the list of image files and annotation files respectively. Make sure that the order or image and annotation in both files are matched. Furthermore, it is advised to use absolute path instead of relative path.
+Example to make the file list in Linux machine
+@code
+ls $PWD/trainset/*.jpg > images_train.txt
+ls $PWD/trainset/*.pts > annotation_train.txt
+@endcode
+
+example of content in the images_train.txt
+@code
+/home/user/helen/trainset/100032540_1.jpg
+/home/user/helen/trainset/100040721_1.jpg
+/home/user/helen/trainset/100040721_2.jpg
+/home/user/helen/trainset/1002681492_1.jpg
+@endcode
+
+example of content in the annotation_train.txt
+@code
+/home/user/helen/trainset/100032540_1.pts
+/home/user/helen/trainset/100040721_1.pts
+/home/user/helen/trainset/100040721_2.pts
+/home/user/helen/trainset/1002681492_1.pts
+@endcode
+
+Creating the facemark object
+---------
+@code
+/*create the facemark instance*/
+FacemarkLBF::Params params;
+params.model_filename = "helen.model"; // the trained model will be saved using this filename
+Ptr<Facemark> facemark = FacemarkLBF::create(params);
+@endcode
+
+Set a custom face detector function
+---------
+Firstly, you need to create your own face detector function, you might also need to create a `struct` to save the custom parameter. Alternatively, you can just make these parameter hard coded within the `myDetector` function.
+@code
+struct Conf {
+    cv::String model_path;
+    double scaleFactor;
+    Conf(cv::String s, double d){
+        model_path = s;
+        scaleFactor = d;
+        face_detector.load(model_path);
+    };
+
+    CascadeClassifier face_detector;
+};
+bool myDetector(InputArray image, OutputArray faces, Conf *conf){
+    Mat gray;
+
+    if (image.channels() > 1)
+        cvtColor(image, gray, COLOR_BGR2GRAY);
+    else
+        gray = image.getMat().clone();
+
+    equalizeHist(gray, gray);
+
+    std::vector<Rect> faces_;
+    conf->face_cascade.detectMultiScale(gray, faces_, conf->scaleFactor, 2, CASCADE_SCALE_IMAGE, Size(30, 30) );
+    Mat(faces_).copyTo(faces);
+    return true;
+}
+@endcode
+
+
+The following snippet demonstrates how to set the custom detector to the facemark object and use it to detect the faces. Keep in mind that some facemark object might use the face detector during the training process.
+
+@code
+Conf config("../data/lbpcascade_frontalface.xml", 1.4);
+facemark->setFaceDetector(myDetector, &config); // we must guarantee proper lifetime of "config" object
+@endcode
+
+Here is the snippet for detecting face using the user defined face detector function.
+
+@code
+Mat img = imread("../data/himym3.jpg");
+std::vector<cv::Rect> faces;
+facemark->getFaces(img, faces, config);
+for(int j=0;j<faces.size();j++){
+    cv::rectangle(img, faces[j], cv::Scalar(255,0,255));
+}
+imshow("result", img);
+waitKey(0);
+@endcode
+
+Training a facemark object
+----
+- First of all, you need to set the training parameters
+    @code
+    params.n_landmarks = 68; // number of landmark points
+    params.initShape_n = 10; // number of multiplier for make data augmentation
+    params.stages_n=5; // amount of refinement stages
+    params.tree_n=6; // number of tree in the model for each landmark point
+    params.tree_depth=5; //he depth of decision tree
+    facemark = FacemarkLBF::create(params);
+    @endcode
+
+- And then, you need to load the file list from the dataset that you have prepared.
+    @code
+    std::vector<String> images_train;
+    std::vector<String> landmarks_train;
+    loadDatasetList("images_train.txt","annotation_train.txt",images_train,landmarks_train);
+    @endcode
+
+- The next step is to add training samples into the facemark object.
+    @code
+    Mat image;
+    std::vector<Point2f> facial_points;
+    for(size_t i=0;i<images_train.size();i++){
+        image = imread(images_train[i].c_str());
+        loadFacePoints(landmarks_train[i],facial_points);
+        facemark->addTrainingSample(image, facial_points);
+    }
+@endcode
+
+- execute the training process
+@code
+/*train the Algorithm*/
+facemark->training();
+@endcode
+
+Use the trained model to detect the facial landmarks from a given image.
+-----
+- First of all, load the trained model. You can also download the pre-trained model in this link <https://raw.githubusercontent.com/kurnianggoro/GSOC2017/master/data/lbfmodel.yaml>
+    @code
+    facemark->loadModel(params.model_filename);
+    @endcode
+
+- Detect the faces
+@code
+facemark->getFaces(img, faces, config);
+@endcode
+
+- Perform the fitting process
+@code
+std::vector<std::vector<Point2f> > landmarks;
+facemark->fit(img, faces, landmarks);
+@endcode
+
+- Display the result
+@code
+for(int j=0;j<faces.size();j++){
+    face::drawFacemarks(img, landmarks[j], Scalar(0,0,255));
+}
+imshow("result", img);
+waitKey(0);
+@endcode