الفهرس 
IntroductionOnly 14 pages are availabe for public view
 |  | from | 89 |  |  |
| | | from | 89 | | |
Abstract
The thesis treats this topic in four chapters, in addition to a
conclusions chapter and a list of references.
Chapter one gives an overview on the scope of the thesis, previous
work, problem definition, motivation, objectives, and the thesis outline. The
chapter justifies the need to use the GPUs. Also it justifies the reasons
behind selecting algorithms to fit on the GPU and the parallel paradigms that
are used in the proposed implementation. The previous work section
includes different attempts to parallelize the face recognition and detection
task on different hardware platforms like FPGA and GPUs and it emphasizes
the advantages and disadvantages of each implementation.
Chapter two introduces the face recognition fundamental techniques.
It discusses well know machine learning techniques like Hidden Markov
Model (HMM) that tries to build a statistical model of transit states to define
the probability of moving from one state to another trying to predict the next
state. Support Vector Machine (SVM) as one of the well knows techniques
in categorization algorithms which tries to find a plane that totally or
partially separate two or more categories. Different SVM models is
explained i.e. soft-margin and kernel function techniques that enhance the
SVM algorithm greatly. It explains Eigen faces and the need to optimize the
Eigen face calculations and the limitations. A full working example was
introduced to explain the whole process to skip that in the implementation
later on chapter three. The face detection algorithm for Viola-Jones is
presented and the algorithm key enhancements are explained in details.
Chapter three discuss the proposed software framework to achieve the
maximum frame rate and keep the scalability as much as required. The
acceleration of face detection phase using GPUs is discussed in details. The
MPI messages that are exchanged among the different nodes and the overall
performance analysis are presented.
Chapter four presented four different use cases with different 4 GPU
models. The use cases showed the performance of the proposed
implementation on different GPUs and CPUs. The Mobile GPU NVidia
M311 showed a good performance for a mobile GPU with 1.74 fps. The
desktop GPUs NVidia GeForce GT240 and GeForce GTX 560 performance
was 6.1 fps and 31.25 fps respectively. The performance on a cluster of four
nodes with GPU NVidia 610 resulted in performance of 5.34 fps. The
performance of the cluster has downgraded due to the use of low end GPU.
Finally the power consumption metric has been calculated and the optimum
power usage and the processing speed related to the power consumption has
been calculated on the four different GPUs to find the most efficient GPU in
terms of power.
Chapter five gives conclusion remarks about the GPU implementation
guidelines for any algorithm, and the research points that are potentially
would achieve more performance on the GPU.