C++ Eye Iris Automatic Detection based on entropy & Iris color score using openCV

An Automatic Eye Iris Detection Method Main phases:

1- Image Preprocessing 2- Selecting Window sizes 3- Entropy Score 4 - Iris Darkness Score 5- Hypothesis Calculation 6- Testing results on a dataset

1- Image Preprocessing
Converting images into grayscale and passing them into Viola & Jones face detector which uses haar-like features so we can focus on our area of interest (faces).

2- Selecting Window sizes:
We set up windows to scan the image, where the iris radius r is approx third the face width. w = 2r + deltaX , h = 2r + deltaY. where deltaX ,deltaY are arbitrary constants added to our random windows. we select the best k windows in terms of entropy score.

3- Entropy Score Entropy is a good measure for uncertainty it's continuous, a strictly convex function, which reaches a maximum value when all probabilities are equal, and maximized in a uniform probability distribution context. Shannon introduced an important concept which is the entropy, in the form H(S)= -∑_(i=1)^n▒〖p(xi)〗 log_2⁡〖p(xi)〗 H score = (Entropy(wi))/(∑_(i=1)^n▒〖Entropy(wi)〗) , where wi is the ith window

4 - Iris Darkness Score
we sum up the pixel values within the range of radius r from the eye center to calculate our iris darkness score C score = 1- (Darkness(wi))/(∑_(i=1)^n▒〖Darkness(wi)〗)

5- Hypothesis Calculation
Our Hypothesis is based on the summation of the two scores T score = H score + C score and we will select the center of the highest scored window as our our iris center.

6- Testing results on a dataset
Our Automatic Iris detector accuracy will be tested using a labeled dataset