OpenCV ships with a tool called traincascade that trains LBP, Haar and HOG. Specifically for face detection they even ship the 3000-image dataset of 24x24 pixel faces, in the format needed by traincascade.
In my experience, of the three types traincascade supports, LBP takes the least time to train, taking on the order of hours rather than days for Haar.
A quick overview of its training process is that for the given number of stages (a decent choice is 20), it attempts to find features that reject as many non-faces as possible while not rejecting the faces. The balance between rejecting non-faces and keeping faces is controlled by the mininum hit rate (OpenCV chose 99.5%) and false alarm rate (OpenCV chose 50%). The specific meta-algorithm used for crafting OpenCV's own LBP cascade is Gentle AdaBoost (GAB).
The variant of LBP implemented in OpenCV is described here:
Shengcai Liao, Xiangxin Zhu, Zhen Lei, Lun Zhang and Stan Z. Li. Learning Multi-scale Block Local Binary Patterns for Face Recognition. International Conference on Biometrics (ICB), 2007, pp. 828-837.
What it amounts to in practice in OpenCV with default parameters is:
OpenCV LBP Cascade Runtime Overview
The detector examines 24x24 windows within the image looking for a face. Stepping from Stage 1 to 20 of the cascade classifier, if it can show that the current 24x24 window is likely not a face, it rejects it and moves over the window by one or two pixels over to the next position; Otherwise it proceeds to the next stage.
During each stage, 3-10 or so LBP features are examined. Every LBP feature has an offset within the window and a size, and the area it covers is fully contained within the current window. Evaluating an LBP feature at a given position can result in either a pass or fail. Depending on whether an LBP feature succeeds or fails, a positive or negative weight particular to that feature is added to an accumulator.
Once all of a stage's LBP features are evaluated, the accumulator's value is compared to the stage threshold. A stage fails if the accumulator is below the threshold, and passes if it is above. Again, if a stage fails, the cascade is exited and the window moves to the next position.
LBP feature evaluation is relatively simple. At that feature's offset within the window, nine rectangles are laid out in a 3x3 configuration. These nine rectangles are all the same size for a particular LBP feature, ranging from 1x1 to 8x8.
The sum of all the pixels in the nine rectangles are computed, in other words their integral. Then, the central rectangle's integral is compared to that of its eight neighbours. The result of these eight comparisons is eight bits (1 or 0), which are assembled in an 8-bit LBP.
This 8-bit bitvector is used as an index into a 2^8 == 256-bit LUT, computed by the training process and particular to each LBP feature, that determines whether the LBP feature passed or failed.
That is all there is to it.
