الفهرس 
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Abstract
A face recognition system based on rough set theory and neural networks is
developed in this thesis. The system consists of three stages; principal component
analysis, rough set theory for dimensionality reduction, and neural network for
recognition. In this thesis use peA for feature projection and reduction, peA has an
apparent limitation it cannot guarantee that a few first selected principal components
are the most adequate features for face recognition. To solution this limitation is apply
Rough set theory for feature selection. Applying the rough set theory to select the
most adequate and discriminative features from the principal components generated
by peA then using LVQ neural network for classification. In this work, a single
network was build for all person. the number of neurons in the output neural network
equal to the number of people to be classified. When a person is recognized, the
neuron corresponding in the last layer will have a value 1, and the other neurons will
have a value O. Using peA and rough sets for feature extraction and selection, then
using LVQ NN for classification shown significant improvement in case of training
time requirement i.e. the classifier network converges faster and the recognition rate
has also increased. The algorithms that have been developed are tested on ORL and
Yale Face Databases.