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 createEigenFaceRecognizer)
-   Fisherfaces (see createFisherFaceRecognizer)
-   Local Binary Patterns Histograms (see createLBPHFaceRecognizer)

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 my 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` with the path to the folder, just like this and you could save the
output:

@code{.sh}
philipp@mango:~/facerec/data$ python create_csv.py
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
