Computer extraction of human faces
Due to the recent advances in visual communication and face recognition
technologies, automatic face detection has attracted a great deal of research interest.
Being a diverse problem, the development of face detection research has comprised
contributions from researchers in various fields of sciences. This thesis examines the
fundamentals of various face detection techniques implemented since the early 70's.
Two groups of techniques are identified based on their approach in applying face
knowledge as a priori: feature-based and image-based.
One of the problems faced by the current feature-based techniques, is the lack of costeffective
segmentation algorithms that are able to deal with issues such as background
and illumination variations. As a result a novel facial feature segmentation algorithm
is proposed in this thesis. The algorithm aims to combine spatial and temporal
information using low cost techniques. In order to achieve this, an existing motion
detection technique is analysed and implemented with a novel spatial filter, which
itself is proved robust for segmentation of features in varying illumination conditions.
Through spatio-temporal information fusion, the algorithm effectively addresses the
background and illumination problems among several head and shoulder sequences.
Comparisons of the algorithm with existing motion and spatial techniques establishes
the efficacy of the combined approach.