Abstract
The design of a neural network based eye tracker is presented. A series of experiments with counterpropagation neural networks convert synthetic video images into eye coordinates by an enhanced feed-forward neural network with multiple winning hidden layer nodes. Difficulties encountered during the design process are discussed. The results show that accurate, fine-grained tracking of a human's eye position is possible by processing the video image collected from a goggle-mounted miniature charge-coupled device (CCD) camera.
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