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 histogram face spaces. Next, a classificationalgorithm, which inherently applies the optimumclassification measure in these spaces, ismathematically derived. The development of thisalgorithm was motivated by the practical limitationsthat impair the performance of the Eigenfaces method.To overcome these limitations, theoretical andexperimental statistical criteria are derived inorder to achieve high recognition rates. Thus, anovel and potent face recognition framework ispresented along with other standard face recognitionmethodologies.
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 histogram face spaces. Next, a classificationalgorithm, which inherently applies the optimumclassification measure in these spaces, ismathematically derived. The development of thisalgorithm was motivated by the practical limitationsthat impair the performance of the Eigenfaces method.To overcome these limitations, theoretical andexperimental statistical criteria are derived inorder to achieve high recognition rates. Thus, anovel and potent face recognition framework ispresented along with other standard face recognitionmethodologies.
The book presents research work on face recognition using edge information as features for face recognition with ICA algorithms. The independent components are extracted from edge information. These...
Fundamentals, Preliminaries and Reviews.- Fundamentals.- Preliminaries and Reviews.- Face Recognition Under Variations.- Symmetric Shape from Shading.- Generalized Photometric Stereo.- Illuminating...
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