Abstract

The automatic detection of person's identity is a very interesting issue both in social and industrial environments. In this paper a system for automatic identity recognition from face images, is presented. The proposed approach is based on an hybrid iconic approach, where a first recognition score is obtained by matching a person's face against an eigen-space obtained from an image ensemble of known individuals. The hypothesis is then verified by computing the correlation of the gray level histogram of the new face image with the histograms of the subjects in the database. A selective attentional mechanism is applied to reduce the amount of information needed to describe a database of human faces. This is accomplished both at the task level, by performing planned fixations, and at the sensor level, by adopting a space-variant sampling of the images. By using a space-variant image geometry, the size of the database is considerably reduced and consequently also the processing time for recognition.

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