This detection is also called "Viola-Jones object detection framework", named after Paul Viola and Michael Jones, which uses Haar Features (which derive from HAAR Wavelets) to detect objects. Haar-like features are features represented as digital images such as lines and edges that are used in object recognition. The Haar classifier uses these digital images to detect objects by viewing the change in contrast values between adjacent rectangular groups of pixels. These changes in contrast determine relative light and dark areas. The reason why these feature are used is because they are easily scaled by increasing or decreasing the size of the pixel group being analysed.
Figure 1. Haar Features
Using the Viola-Jones framework, the features that are used involves the sums of the image pixels within the rectangular areas. While there are other classifiers that use Haar such as the Haar Basis Function, the Viola-Jones uses more than one rectangular area making it more complex and therefore able to detect more facial features
This Viola-Jones framework is the method included with the current OpenCV libraries for facial detection.
References
Michael Jones, Paul Viola. Robust Real-time Object Detection. 2001. Second International Workshop on Statistical and Computational Theories of Vision - Modeling, Learning, Computing and Sampling.
(http://research.microsoft.com/en-us/um/people/viola/Pubs/Detect/violaJones_IJCV.pdf)
Dr John Fernandez, Phillip Ian Wilson. Facial Feature Detection Using HAAR Classifiers. 2006. JCSC 21, 4.
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