The computer vision problem of face recognition has over the years
become a common high-requirement benchmark for machine learning
methods. In the last decade, highly efficient face recognition
systems have been developed that extensively use the nature of the
image domain to achieve accurate real-time performance. The
effectiveness of such systems are possible only with the progress
of machine learning algorithms. Support vector machine learning is
a relatively recent method that offers a good generalization
performance in classification problems like face recognition. An
algorithm based on Gabor texture information with SVM classifier is
demonstrated in this book.The estimated model parameters serve as
texture representation and experiments were performed on Yale, ORL
and FERET databases to validate the feasibility of the method. The
results showed that both Gabor magnitude and Gabor phase based
texture representation technique with SVM classifier significantly
outperformed the widely used Gabor energy based systems and other
existing subspace methods. In addition, the feature level fusion of
these two kinds of texture representations performs better than
when used individually
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