Nowadays, there is growing interest in face detection applications for
unconstrained environments. The increasing need for public security and national
security motivated our research on the automatic face detection system. For public
security surveillance applications, the face detection system must be able to cope
with unconstrained environments, which includes cluttered background and
complicated illuminations. Supervised approaches give very good results on
constrained environments, but when it comes to unconstrained environments, even
obtaining all the training samples needed is sometimes impractical. The limitation of
supervised approaches impels us to turn to unsupervised approaches.
In this thesis, we present an efficient and unsupervised face detection system,
which is feature and configuration based. It combines geometric feature detection
and local appearance feature extraction to increase stability and performance of the
detection process. It also contains a novel adaptive lighting compensation approach
to normalize the complicated illumination in real life environments. We aim to
develop a system that has as few assumptions as possible from the very beginning, is
robust and exploits accuracy/complexity trade-offs as much as possible. Although
our attempt is ambitious for such an ill posed problem-we manage to tackle it in the
end with very few assumptions.
A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.