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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/2756

Authors: Sentayehu, Endeshaw Wolde
Advisors: Dr. Kumudha Raimond
Keywords: Gabor filter and principal component analysis
artificial neural network
Face recognition
Copyright: Aug-2006
Date Added: 7-May-2012
Publisher: AAU
Abstract: In recent years, an explosion in research on pattern recognition systems using neural network methods has been observed. Face Recognition (FR) is a specialized pattern recognition task for several applications such as security: access to restricted areas, banking: identity verification and recognition of wanted people at airports. This thesis will explain what is involved in FR task and outline a complete Face Recognition System (FRS) based on Artificial Neural Network (ANN). In this work, two FRS are developed. The first model uses Principal Component Analysis (PCA) for feature extraction from the face images and ANN for the classification purpose. In the second model, combination of Gabor Filter (GF) and PCA are used for feature extraction and ANN for the classification. In the first approach, the face images are projected into subspace called eigenspace, consisting of the eigenvectors from the covariance matrix of the face images. The projection of an image into eigenspace will transform the image into a representation of a lower dimension which aims to hold the most important features of the face. These feature vectors are classified into training, validation and testing set. The training and validation set are used during the training of ANN. The testing set is used to evaluate the recognition performance of the model. In the second approach, Gabor feature vectors are derived from a set of downsampled Gabor wavelet representations of face images, then the dimensionality of the vectors is reduced by means of Principal Component Analysis (PCA), and finally ANN is used for classification. The Gabor filtered face images exhibit strong characteristics of spatial locality, scale, and orientation selectivity. These images can, thus, produce salient local features that are most suitable for FR. Experimentation is carried out on FRS by using Olivetti Research Laboratory (ORL) datasets, the images of which vary in illumination, expression, pose and scale. The result shows the feasibility of the methodology followed in this thesis work. Model 1 achieves a recognition rate of 76.6% whereas model 2 achieves 88.3% of correct classification and performed very efficiently when subjected to new unseen images with a false rejection rate of 0% during testing. The high recognition rate of model 2 shows the efficiency of GF in feature extraction.
URI: http://hdl.handle.net/123456789/2756
Appears in:Thesis - Computer Engineering

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