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We present the development and implementation of a face recognition system using principal component analysis to construct a face model from a training set of face images. The computed principal component face model is applied in the feature extraction task. We tested the system in the task of correctly classifying 435 images from 102 people with a minimum distance classifier. These images have a great deal of variations concerning position, scale, expression and illumination, even among images of the same person. The proposed system was able to handle such variations and achieved a recognition rate of 97.70%.
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