Face Recognition Techniques and Analysis
Classification in Principal and Histogram Spaces
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This book presents standard as well as novel facerecognition methods. These methods utilize PrincipalComponent Analysis, Linear Discriminant Analysis,Independent Component Analysis, Gabor Wavelets,Neural Networks, Hidden Markov Models, GraphMatching, etc. Emphasis is given to the popularEigenfaces algorithm, which is presented analyticallyin detail and a framework is presented for itsexperimental evaluation. In addition, this bookpresents a novel face recognition method that iscomputationally efficient and can be implemented as areal-time process. This method operates in quantizedblock histogr...
This book presents standard as well as novel face
recognition methods. These methods utilize Principal
Component Analysis, Linear Discriminant Analysis,
Independent Component Analysis, Gabor Wavelets,
Neural Networks, Hidden Markov Models, Graph
Matching, etc. Emphasis is given to the popular
Eigenfaces algorithm, which is presented analytically
in detail and a framework is presented for its
experimental evaluation. In addition, this book
presents a novel face recognition method that is
computationally efficient and can be implemented as a
real-time process. This method operates in quantized
block histogram face spaces. Next, a classification
algorithm, which inherently applies the optimum
classification measure in these spaces, is
mathematically derived. The development of this
algorithm was motivated by the practical limitations
that impair the performance of the Eigenfaces method.
To overcome these limitations, theoretical and
experimental statistical criteria are derived in
order to achieve high recognition rates. Thus, a
novel and potent face recognition framework is
presented along with other standard face recognition
methodologies.
recognition methods. These methods utilize Principal
Component Analysis, Linear Discriminant Analysis,
Independent Component Analysis, Gabor Wavelets,
Neural Networks, Hidden Markov Models, Graph
Matching, etc. Emphasis is given to the popular
Eigenfaces algorithm, which is presented analytically
in detail and a framework is presented for its
experimental evaluation. In addition, this book
presents a novel face recognition method that is
computationally efficient and can be implemented as a
real-time process. This method operates in quantized
block histogram face spaces. Next, a classification
algorithm, which inherently applies the optimum
classification measure in these spaces, is
mathematically derived. The development of this
algorithm was motivated by the practical limitations
that impair the performance of the Eigenfaces method.
To overcome these limitations, theoretical and
experimental statistical criteria are derived in
order to achieve high recognition rates. Thus, a
novel and potent face recognition framework is
presented along with other standard face recognition
methodologies.
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