Local Binary Patterns

My website recently saw a lot of hits for a tiny wiki page on Local Binary Patterns, so I thought it might be useful to share some code. I am preparing a long blog post on Local Binary Patterns (LBP), but you know it... 80% done and 20% left to do... Damn Pareto strikes again! For now I'll make the long story a bit shorter.

All source code in this post is available at:

It comes as a CMake project, so to build and run it simply type:

philipp@mango:~/some/dir$ mkdir build; cd build
philipp@mango:~/some/dir/build$ cmake ..
philipp@mango:~/some/dir/build$ make
philipp@mango:~/some/dir/build$ ./lbp  

If you are in desperate need of plain C or Python versions, then just comment below or drop me a mail. This should give you an idea for speeding up the Python implementations. And if I forgot anything important, I am sure the great Scholarpedia page on Local Binary Patterns answers your questions.


If you have read my blog posts on Fisherfaces or Eigenfaces I've left you with the impression everything works just fine. You should have been sceptic. If you've ever developed algorithms for real life you know one thing for sure: never expect a perfect world. Let's revisit the algorithms we've discussed. Both the Fisherfaces and Eigenfaces method 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 crap, so a lower-dimensional subspace is identified, where (probably) useful information is preserved. We've already seen that the Eigenfaces approach is likely to find the wrong components on images with a lot of variation in illumination, because components with a maximum variance over all classes aren't necessarily useful for classification. So to preserve some discriminative information we applied a Linear Discriminant Analysis (with the Fisher criterion) and optimized as described in the Fisherfaces method. The Fisherfaces method worked great... at least for the very 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 images of a person. So what if there's only one image for each person? Our covariance estimates for the subspace will be horribly wrong, something also known as the Small Sample Size Problem. 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? I've put a tiny script for you into the appendix, feel free to experiment with. Running the script on the AT&T Facedatabase, which is a fairly easy image database, shows:


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; somewhat logical when the subspace we project our data into is identified by a PCA. You can find similar figures in often cited papers.

All this is known for ages and you can find tons of literature discussing these problems. Some papers try to regulate the Discriminant Analysis or use the pseudo-inverse within-scatter matrix, just search Google Scholar on Small Sample Size. Now this is just my personal and totally biased take on this: I think the error you should expect with this kind of models is way too high (and I haven't seen an implementation yet). Other solutions instead assume a face model and synthesize images from a given sample. One thing all solutions have in common is, that they are much too complicated for my brain.

local descriptions and local binary patterns

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.

You've seen those cool SIFT Features in OpenCV? The algorithm extracts local keypoints in your image that doesn't mind the scale, one of the reasons why it's called Scale-invariant feature transform. Just like SIFT, the Local Binary Patterns methodology has its roots in 2D texture analysis. The basic idea 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. With 8 surrounding pixels you'll end up with 2^8 possible combinations, which are called Local Binary Patterns or sometimes abbreviated as LBP codes. The first LBP operator actually used a fixed 3 x 3 neighborhood just like this:


So why is this description so great? Because it's dead simple to implement and fast as hell. I'll just name it Original LBP in code:

template <typename _Tp>
void lbp::OLBP_(const Mat& src, Mat& dst) {
    dst = Mat::zeros(src.rows-2, src.cols-2, CV_8UC1);
    for(int i=1;i<src.rows-1;i++) {
        for(int j=1;j<src.cols-1;j++) {
            _Tp center =<_Tp>(i,j);
            unsigned char code = 0;
            code |= (<_Tp>(i-1,j-1) > center) << 7;
            code |= (<_Tp>(i-1,j) > center) << 6;
            code |= (<_Tp>(i-1,j+1) > center) << 5;
            code |= (<_Tp>(i,j+1) > center) << 4;
            code |= (<_Tp>(i+1,j+1) > center) << 3;
            code |= (<_Tp>(i+1,j) > center) << 2;
            code |= (<_Tp>(i+1,j-1) > center) << 1;
            code |= (<_Tp>(i,j-1) > center) << 0;
  <unsigned char>(i-1,j-1) = code;

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. The idea is to align an abritrary number of neighbors on a circle with a variable radius, which enables to capture the following neighborhoods:


The operator is an extension to the original LBP codes, so it gets called the Extended LBP (sometimes 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, I'll simply do a bilinear interpolation:

template <typename _Tp>
void lbp::ELBP_(const Mat& src, Mat& dst, int radius, int neighbors) {
    neighbors = max(min(neighbors,31),1); // set bounds...
    // Note: alternatively you can switch to the new OpenCV Mat_
    // type system to define an unsigned int matrix... I am probably
    // mistaken here, but I didn't see an unsigned int representation
    // in OpenCV's classic typesystem...
    dst = Mat::zeros(src.rows-2*radius, src.cols-2*radius, CV_32SC1);
    for(int n=0; n<neighbors; n++) {
        // sample points
        float x = static_cast<float>(radius) * cos(2.0*M_PI*n/static_cast<float>(neighbors));
        float y = static_cast<float>(radius) * -sin(2.0*M_PI*n/static_cast<float>(neighbors));
        // relative indices
        int fx = static_cast<int>(floor(x));
        int fy = static_cast<int>(floor(y));
        int cx = static_cast<int>(ceil(x));
        int cy = static_cast<int>(ceil(y));
        // fractional part
        float ty = y - fy;
        float tx = x - fx;
        // set interpolation weights
        float w1 = (1 - tx) * (1 - ty);
        float w2 =      tx  * (1 - ty);
        float w3 = (1 - tx) *      ty;
        float w4 =      tx  *      ty;
        // iterate through your data
        for(int i=radius; i < src.rows-radius;i++) {
            for(int j=radius;j < src.cols-radius;j++) {
                float t = w1*<_Tp>(i+fy,j+fx) + w2*<_Tp>(i+fy,j+cx) + w3*<_Tp>(i+cy,j+fx) + w4*<_Tp>(i+cy,j+cx);
                // we are dealing with floating point precision, so add some little tolerance
      <unsigned int>(i-radius,j-radius) += ((t ><_Tp>(i,j)) && (abs(<_Tp>(i,j)) > std::numeric_limits<float>::epsilon())) << n;

Now using an abritrary radius and sample points has two effects. With an educated guess I would say... The more sampling points you take, the more patterns you can encode, the more discriminative power you have, but the higher the computational effort. Instead the larger the radius, the smoother the image, the larger details can be captured, the less discriminative power the description has (if you don't increase the sampling points at the same time). Let's see what the LBP codes look like given a sample frame. My webcam isn't really high-resolution, please don't laugh at my hardware!

Radius Sampling Points LBP Image
14 dummy
18 dummy
116 dummy
24 dummy
28 dummy
216 dummy
44 dummy
48 dummy
416 dummy

Now what's left is how to classify an object. If you would throw all features into a single histogram all spatial information is discarded. In tasks like face detection (and a lot of other pattern recognition problems) spatial information is very useful, so it has to be incorporated into the histogram somehow. The representation proposed by Ahonen et al. in Face Recognition with Local Binary Patterns is to divide the LBP image into grids and build a histogram of each cell seperately. Then by concatenating the histograms the spatial information is encoded (not merging them), just like this:


This informal description translates to OpenCV as:

And here's the Local Binary Patterns Histograms Face Recognizer:


The experiment was done with facerec version number 0.1. If you want to recreate them you either download the tag or you adapt the script to the most recent version (which should be very easy).

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