Face recognition using Eigenfaces and neural networks

Akalın, Volkan
A face authentication system based on principal component analysis and neural networks is developed in this thesis. The system consists of three stages; preprocessing, principal component analysis, and recognition. In preprocessing stage, normalization illumination, and head orientation were done. Principal component analysis is applied to find the aspects of face which are important for identification. Eigenvectors and eigenfaces are calculated from the initial face image set. New faces are projected onto the space expanded by eigenfaces and represented by weighted sum of the eigenfaces. These weights are used to identify the faces. Neural network is used to create the face database and recognize and authenticate the face by using these weights. In this work, a separate network was build for each person. The input face is projected onto the eigenface space first and new descriptor is obtained. The new descriptor is used as input to each person̕s network, trained earlier. The one with maximum output is selected and reported as the host if it passes predefined recognition threshold. The algorithms that have been developed are tested on ORL, Yale and Feret Face Databases.


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Citation Formats
V. Akalın, “Face recognition using Eigenfaces and neural networks,” M.S. - Master of Science, Middle East Technical University, 2003.