Eigenfaces Versus Eigeneyes: First Steps Toward Performance Assessment of Representations for Face Recognition

@inproceedings{Campos2000EigenfacesVE,
  title={Eigenfaces Versus Eigeneyes: First Steps Toward Performance Assessment of Representations for Face Recognition},
  author={Te{\'o}filo Em{\'i}dio de Campos and Rog{\'e}rio Schmidt Feris and Roberto Marcondes Cesar Junior},
  booktitle={MICAI},
  year={2000}
}
The Principal Components Analysis (PCA) is one of the most successfull techniques that have been used to recognize faces in images. This technique consists of extracting the eigenvectors and eigenvalues of an image from a covariance matrix, which is constructed from an image database. These eigenvectors and eigenvalues are used for image classification, obtaining nice results as far as face recognition is concerned. However, the high computational cost is a major problem of this technique… CONTINUE READING
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