A NEURAL MODEL OF FACE RECOGNITION: A COMPREHENSIVE APPROACH

Visual recognition of faces is an essential behavior of humans: we have optimal performance in everyday life and just such a performance makes us able to establish the continuity of actors in our social life and to quickly identify and categorize people. This remarkable ability justifies the general interest in face recognition of researchers belonging to different fields and specially of designers of biometrical identification systems able to recognize the features of person's faces in a background.

Due to interdisciplinary nature of this topic in this contribute we deal with face recognition through a comprehensive approach with the purpose to reproduce some features of human performance, as evidenced by studies in psychophysics and neuroscience, relevant to face recognition. This approach views face recognition as an emergent phenomenon resulting from the nonlinear interaction of a number of different features. For this reason our model of face recognition has been based on a computational system implemented through an artificial neural network.

This synergy between neuroscience and engineering efforts allowed us to implement a model that had a biological plausibility, performed the same tasks as human subjects, and gave a possible account of human face perception and recognition. In this regard the paper reports on an experimental study of performance of a SOM-based neural network in a face recognition task, with reference both to the ability to learn to discriminate different faces, and to the ability to recognize a face already encountered in training phase, when presented in a pose or with an expression differing from the one present in the training context.