Best Regression for Eye Recognition

Abstract

     Human eye offers a number of opportunities for biometric recognition. The essential parts of the eye like cornea, iris, veins and retina can determine different characteristics. Systems using eyes’ features are widely deployed for identification in government requirement levels and laws; but also beginning to have more space in portable validation world.

The first image was prepared to be used and monitored using CLAHE which means (Contrast Limited Adaptive Histogram Equalization) to improve the contrast of the image, after that the 3D surface plot was created for this image then different types of regression were used and the better one was chosen.

The results showed that power regression is better, and fitter than other fitting methods (8^th, 7^th, 6^th, 5^th, 4^th, 3^rd, 2^nd) degree polynomial, and straight line respectively, when depending on the sum of residual squared.

The estimations of R-square demonstrated that (5^th, 6^th, 7^th, 8^th) have a great proportion of variance in the model followed by (power, 4^th, 3^rd, 2^nd, straight line) respectively.

The conclusion from these results is that the power regression has a better fitting than other types of fitting functions for this study and similar ones.