COMBINING IMAGE PROCESSING OPERATORS AND NEURAL NETWORKS IN A FACE RECOGNITION SYSTEM

Abstract

This paper describes a system able to recognize human faces from different perspectives, and which have different expressions. It possibly presents some kind of noise in their representation. The problem of face recognition has been approached using a complex architecture based on a hierarchy of neural networks, with a particular self-referencing structure. The system, in fact, is structured as a tree in which nodes correspond to neural networks, each one having different tasks. Each leaf is a recognition module composed by some networks with different characteristics depending on the different preprocessing operators used. These networks are coordinated by a supervisor in a self-referencing structure. During the training phase, the supervisor, called Meta-Net, observes the behaviour of recognition nets and learns which net is more able in which task, while during the test phase it decides, given an input image, which weights to assign to each network and modifies their output in order to obtain the final result. This architecture shows a high generalization capability and allows the recognition of images with different kinds of noise better than what each single network can do, as confirmed by a preliminary experimental evaluation.