Illumination invariant face detection

The purpose of face detection is to process input images in order to determine the locations of any faces in the image. 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 thesis reviews the state of the art face detection methods (with particular emphasis on the method of Viola and Jones) and explores methods of overcoming adverse illumination conditions. These methods can be broadly classified as invariant features, normalization and variation modelling. Four novel approaches to overcoming illumination that fall into these 3 categories are proposed in this thesis, namely: (i) log-ratio Haar-like features; (ii) DC Haar-likefeatures; (iii) local variance normalisation; and (iv) classifier fusion. Furthermore, a new type of feature called the generalised integral image feature (GIIF) is proposed as an alterative to Haarlike features. The GIIF method is not specifically related to illumination invariant face detection, but instead applies to the more general task of face detection and is therefore presented as a separate chapter. Experimental results on standard face databases are provided for all of the proposed methods to verify that they achieve improved accuracy.