The purpose of face detection is to
process input images in order to determine the locations of
any faces in the image. Human faces are complex objects
and detecting them remains a challenging task for computer
vision systems, despite the relative ease with which humans
are able to do so. One of the major difficulties faced by
face detection systems is challenging illumination
conditions, such as low level lighting and cast shadows.
This book reviews the state of the art face detection
methods (with particular emphasis on the popular method of
Viola and Jones) and explores methods of overcoming
adverse illumination conditions. These methods can be
broadly classified as invariant features, normalisation and
variation modelling. Each of these approaches to overcoming
illumination is individually explored in this book, and
experimental results on publicly available face databases
are provided to compare their
accuracy.