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.