A Neural Network Approach for Classifying Eye Shape by Features Obtained from the Subjective Evaluation Standard

Abstract

A neural net approach is proposed to classify the eye shape with the respect to the three dominant features obtained from the subjective evaluation standard. The neural network is composed of eighteen physical features of the eye used as input data and the three features in the subjective tests used as output data. From the experiments, it can be concluded that these features combined can best represent various subjective impressions of human eyes. It can also be shown that unknown data can be classified by using k-neighbor interpolation training, where neighboring data are chosen based on the criterion of absolute standard of learning data.