A Visual Perceiving and Eyeball-Motion Controlling Neural Network for Object Searching and Locating
This paper proposes a visual cognitive neural network for automatic object searching and locating. The model consists of two sub-networks. One is a visual perceiving network, which simulates human eyes to input image signals and recognize an object's direction and distance in terms of a high-level perceiving neuron's maximum response. The other one is an eyeball-motion controlling network, which simulates that human brain's high-level perceiving neurons transfer their responses to eyeball-motion controlling muscle cells to change eye's gaze to the position of the object that the perceiving system is attentive to or interested in. The system is applied to human face features searching and experiments show a promising result.