الفهرس 
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Abstract
Detection of Human faces within images plays an important role in many
facial image- related applications such as face recognition/verification,
facial expression analysis, pose normalization, and 3D face reconstruction.
The performance of these applications is usually to a large degree
dependent on accuracy of face detector. Detection of faces is easy
for human beings; however, for machines it is not an easy task at all.
The difficulty comes from high inter-personal variation (e.g., gender,
race), intra-personal changes (e.g., pose, expression), and from acquisition
conditions (e.g., lighting, image resolution). Despite the considerable
amount of the previous studies on the subject, face detection is
not completely solved and it remains a very challenging task. The existing
methods need improvements in their accuracy, and a novel robust
method that can work under various imaging conditions are required. In
this thesis, we propose a new approach that brings us a step closer to this
goal.
The principal objective of this thesis is to investigate a novel approach
toward robust automatic face detector in uncontrolled imaging
conditions. We propose an efficient approach that efficiently combines
generalized Hough transform by Random decision Forests. Random
forests (RF) have become a popular technique for classification, prediction,
studying variable importance, variable selection, and outlier detection.
There are numerous successful applications examples of RF in
a variety of fields, such as object detection, pedestrian detection, and
object tracking and action recognition. Random Forests are probabilistically
efficient technique that can operate quickly over large datasets.